Reviewerly/exHarmony huggingface dataset:
Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: a case of CSIEC chatbot

Artificial Intelligence (AI) technology in the educational context, particularly chatbotics, has made significant changes in learning English. This mixed-methods study is intended to explore university students’ attitudes toward the potential role of artificial intelligence (AI)-assisted mobile applications. Meanwhile, the role of social presence and human-likeness on learner motivation was examined through a chatbot lens. A total of 256 English as a foreign language (EFL) learners interacted with a chatbot known as Computer Simulation in Educational Communication (CSIEC). Participants’ audio-recorded practices, transcriptions, three scales of social presence, learner motivation, and human-likeness, along with a semi-structured focus group interview, were used to collect data, Structural Equation Modeling (SEM) was used for data analysis. Moreover, thematic analysis was adopted to explore the participants’ attitudes and perceptions toward using CSIES. The quantitative results indicated that learner motivation was significantly predicted by social presence and human-likeness. The thematic analysis of qualitative data reflected that the attributed descriptions to the CSIEC teacher enhanced learners’ motivation, eagerness, and confidence to learn English. The findings of this study may be used to guide future research in using chatbots outside the classroom to serve as learning companions, and educators can utilize them to tailor assessment and feedback procedures.
Acceptance of Virtual Reality Exergames Among Chinese Older Adults

It is well documented that exergames are enjoyable to play and can significantly improve older adults’ health and well-being. However, there is limited research on exploring factors affecting these users’ acceptance of such games, especially in virtual reality (VR), a relatively newer technology. This study proposes an extended version of the Technology Acceptance Model (TAM). We use variables from TAM related to older Chinese adults and specific to VR exergames to explore and confirm critical factors that could influence these users’ acceptance of such games in VR. We tested the proposed model with 51 older Chinese adults (aged 65 and above) after playing three commercial VR exergames (Beat Saber, FitXR, Dance Central). Results show that these older adults who are younger and retired and have a higher education, better financial means, and a good health condition have a more positive view of VR exergames. In addition, Perceived Usefulness, Perceived Ease of Use, and Perceived Enjoyment positively affect the intention to play VR exergames. Self-Satisfaction has a positive impact on Perceived Ease of Use and Perceived Usefulness. However, unlike previous studies, our results suggest that Facilitating Conditions have a negative effect on Perceived Ease of Use. Finally, we discuss the theoretical and practical implications of our results.
Central bank digital currencies: An agenda for future research

Central bank digital currencies are engendering concern. As understanding of CBDCs is very limited, further research is warranted which will focus not only on the economic rationale of CBDCs but also on how they will impact monetary policy transmission, financial and price stability, inflation targeting, unconventional monetary instruments, central banks as lenders of last resort, and provision of forward guidance. There are also unsettled questions regarding ethics, privacy and environmental and technological constraints. With the imminent implementation of CBDCs, it is vital to explore these issues.
Effective Use of Likert Scales in Visualization Evaluations: A Systematic Review

Abstract Likert scales are often used in visualization evaluations to produce quantitative estimates of subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to collect, analyze, and visualize data collected with Likert scales are inconsistent among evaluations in visualization papers. In this paper, we examine the use of Likert scales as a tool for measuring subjective response in a systematic review of 134 visualization evaluations published between 2009 and 2019. We find that papers with both objective and subjective measures do not hold the same reporting and analysis standards for both aspects of their evaluation, producing less rigorous work for the subjective qualities measured by Likert scales. Additionally, we demonstrate that many papers are inconsistent in their interpretations of Likert data as discrete or continuous and may even sacrifice statistical power by applying nonparametric tests unnecessarily. Finally, we identify instances where key details about Likert item construction with the potential to bias participant responses are omitted from evaluation methodology reporting, inhibiting the feasibility and reliability of future replication studies. We summarize recommendations from other fields for best practices with Likert data in visualization evaluations, based on the results of our survey. A full copy of this paper and all supplementary material are available at https://osf.io/exbz8/ .
Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution

The effective analytical processing of pathological images is crucial in promoting the development of medical diagnostics. Based on this matter, in this research, a multi-level thresholding segmentation (MLTS) method based on modified different evolution (MDE) is proposed. The MDE is the primary benefit offered by the suggested MLTS technique, which is a novel proposed evolutionary algorithm in this article with significant convergence accuracy and the capability to leap out of the local optimum (LO). This optimizer came into being mostly as a result of the incorporation of the movement mechanisms of white holes, black holes, and wormholes into various evolutions. Thus, the developed MLTS approach may provide high-quality segmentation results and is less susceptible to segmentation process stagnation. To validate the efficacy of the presented approaches, first, the performance of MDE is validated using 30 benchmark functions, and then the proposed segmentation method is empirically compared with other comparable methods using standard pictures. On the basis of breast cancer and skin cancer pathology images, the developed segmentation method is compared to other competing methods and experimentally validated in further detail. By analyzing experimental data, the key compensations of MDE are proven, and it is experimentally shown that the unique MDE-based MLTS approach can achieve good performance in terms of many performance assessment indices. Consequently, the proposed method may offer an efficient segmentation procedure for pathological medical images.
CAAN: Context-Aware attention network for visual question answering

Understanding multimodal information is the key to visual question answering (VQA) tasks. Most existing approaches use attention mechanisms to acquire fine-grained information understanding. However, these approaches with merely attention mechanisms do not solve the potential understanding bias problem. Hence, this paper introduces contextual information into VQA for the first time and presents a context-aware attention network (CAAN) to tackle the case. By improving the modular co-attention network (MCAN) framework, CAAN’s main work includes: designing a novel absolute position calculation method based on the coordinates of each image region in the image and the image’s actual size, the position information of all image regions are integrated as contextual information to enhance the visual representation; based on the question itself, several internal contextual information representations are introduced to participate in the modeling of the question words, solving the understanding bias caused by the similarity of the question. Additionally, we also designed two models of different scales, namely CAAN-base and CAAN-large, to explore the effect of the field of view on interaction. Finally, extensive experimental results show that CAAN significantly outperforms MCAN and achieves comparable or even better performance than other state-of-the-art approaches, proving our method can tackle the understanding bias.
An efficient double-image encryption and hiding algorithm using a newly designed chaotic system and parallel compressive sensing

In this paper, an efficient double-image visually meaningful encryption algorithm combining a new chaotic system, parallel compressive sensing (PCS) and fast Fourier transform (FFT), is proposed. First, Arnold scrambling is performed on two plain images after wavelet transform. Then, the scrambled secret images are compressed by applying a key-controlled partial Hadamard matrix. Finally, the final cipher image with the same resolution as the plain image is acquired by embedding the two encrypted and measured secret images into a carrier image through FFT. To achieve a higher level of security, a new chaotic system combining infinite collapses and a tent map (ICTM) is introduced for disturbing the strong correlation of images and randomly embedding encrypted data into carrier images. The parameters of ICTM are generated by the hash value of the plain images, which enhances the ability of the cryptographic system to resist plaintext attacks. Moreover, the parameters related to the plaintext are embedded into the alpha channel of the visually meaningful cipher image to reduce unnecessary key transmission. The simulation results and performance analysis show that the cryptosystem is feasible and efficient under the condition of guaranteeing all the evaluation indices.
An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.
A paradigm of blockchain and supply chain performance: a mediated model using structural equation modeling

Purpose This paper aims to find the impact of blockchain supply chain on supply chain performance with a mediating role of supplier trust, traceability and transparency. Design/methodology/approach Data was collected using the purposive sampling technique on a five-point Likert scale from 150 respondents. For data analysis, IBM SPSS and Smart PLS 3.3.7 were used to test the hypotheses by evaluating the structural equation modeling. Findings The blockchain supply chain found a significant effect on supply chain performance. Moreover, there is a substantial effect of the blockchain supply chain on supply chain performance via mediators, including supplier trust, supply chain traceability and supply chain transparency. Further, the mediation type of all mediators was full mediation in the relationships between blockchain supply chain and supply chain performance. Research limitations/implications The research findings are helpful for industrialists, supply chain practitioners and policymakers. The practitioners can adopt blockchain technology to enhance inter-organizational collaboration, develop trust, data visibility and traceability, and critical decisions, ultimately bringing sustainable growth for the firm. Originality/value The outcomes of this research enrich the literature and share the impact of one of the most trending technologies in the supply chain perspective. Future research can empirically test the relationship between blockchain and supply chain sustainability.
Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy

Domain generalization (DG) methods have been successfully proposed to enhance the generalization ability of the intelligent diagnosis model. However, these methods hardly achieve data privacy-preserving generalization diagnosis tasks due to potential conflicts of interest or data privacy protocol. To this end, this study proposes a novel federated adversarial DG network for machinery fault diagnosis. The collaborative training between the central server and several clients is implemented in the proposed network, which aims to build a global fault diagnosis model for multiple clients under data privacy conditions. To eliminate the distribution discrepancies of different clients, a multiclient feature alignment module with adversarial learning is designed. In this module, the generative adversarial network with class-wise information is introduced to generate a reference distribution adaptatively, and the adversarial training strategy between reference distribution and real distribution is implemented to learn the DG features from different clients. Experimental results on three cases show that the proposed network can achieve more than 10% performance improvement for diagnosis methods with federated learning. The small performance gap between the proposed network and the no-federated learning method suggests that the proposed network is promising for solving data privacy-preserving generalization diagnosis tasks.
A complex Jensen–Shannon divergence in complex evidence theory with its application in multi-source information fusion

Multi-source information fusion has attracted considerable attention in the few past years and plays a great role in real applications. However, the uncertainty or conflict will make the fusion results unreasonable. Furthermore, the information may be collected in the form of complex number that cannot be processed by existing methods. In this article, to handle the above issues, the complex evidence theory (CET) is exploited. CET is the generalization of Dempster–Shafer evidence theory, where the mass function is modeled by complex number, called complex mass function (CMF). In order to deal with multi-source information fusion from the perspective of the complex plane, a new Complex Jensen–Shannon divergence (CJS divergence) is presented in this article. The proposed CJS divergence can effectively measure the conflict between two CMFs, and it satisfies the properties of boundedness, symmetry and nondegeneracy. In addition, for a better combination result, we have adjusted the complex Dempster’s rule of combination, which is called the reinforced complex evidence combination rule (RCECR). Then an algorithm for multi-source information fusion is proposed based on the CJS divergence and the RCECR. Some numerical examples and two applications in target identification and medical diagnosis illustrate the effectiveness of the new approach.
DDoS Attack Prevention for Internet of Thing Devices Using Ethereum Blockchain Technology

The Internet of Things (IoT) has widely expanded due to its advantages in enhancing the business, industrial, and social ecosystems. Nevertheless, IoT infrastructure is susceptible to several cyber-attacks due to the endpoint devices' restrictions in computation, storage, and communication capacity. As such, distributed denial-of-service (DDoS) attacks pose a serious threat to the security of the IoT. Attackers can easily utilize IoT devices as part of botnets to launch DDoS attacks by taking advantage of their flaws. This paper proposes an Ethereum blockchain model to detect and prevent DDoS attacks against IoT systems. Additionally, the proposed system can be used to resolve the single points of failure (dependencies on third parties) and privacy and security in IoT systems. First, we propose implementing a decentralized platform in place of current centralized system solutions to prevent DDoS attacks on IoT devices at the application layer by authenticating and verifying these devices. Second, we suggest tracing and recording the IP address of malicious devices inside the blockchain to prevent them from connecting and communicating with the IoT networks. The system performance has been evaluated by performing 100 experiments to evaluate the time taken by the authentication process. The proposed system highlights two messages with a time of 0.012 ms: the first is the request transmitted from the IoT follower device to join the blockchain, and the second is the blockchain response. The experimental evaluation demonstrated the superiority of our system because there are fewer I/O operations in the proposed system than in other related works, and thus it runs substantially faster.
Developing an AI-based chatbot for practicing responsive teaching in mathematics

Responsive teaching promotes students' mathematical reasoning and positive attitudes toward mathematics. Due to the complexity of the work of teaching, preservice teachers (PSTs) have been provided with approximated opportunities to practice responsive teaching skills in teacher education programs. Although increasing demand for adaptive learning reinforces the need for research on artificial intelligence (AI) in education, there have been few approaches that engaged learners in meaningful interactions. Our goal was to develop an AI-based chatbot that engaged PSTs in an authentic, meaningful, and open-ended teaching situation to enhance PSTs' responsive teaching skills, specifically questioning skills through approximations of practice. The chatbot was designed to act as a virtual student who displayed misconceptions on the topic of fractions. By employing design-based research, we examined 1) design features and structure of the chatbot, 2) coverage of users' input, 3) PSTs' questioning patterns, and 4) users' experiences. Two iterations of design, implementation and evaluation took place in an elementary mathematics education methods course. To build the chatbot we qualitatively analyzed the training data, categorized them into the smallest meaningful intents of users, and prepared corresponding responses to each intent. At the final iteration, the refined chatbot adequately covered PSTs’ questions and provided realistic responses. We found a pattern of PSTs asking similar questions repeatedly in the conversation data. Through multiple iterations, certain design features could lead to improved questioning patterns and user perceptions, including sequential responses, informing responses, and personification. Implications, design features, and limitations are discussed.
Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.
Securing Smart Cities Using Blockchain Technology

Recent years have witnessed substantial advancement in the subject of smart cities. The purpose of smart city development is to enhance the quality of life for citizens. This was accomplished via the use of IoT and cloud computing technologies. Another promising technology is blockchain, which has the ability to deliver a myriad of value services to its end users. For virtual assets with value, such as Bitcoin, it is a programmable digital register that is immutable. To properly exploit blockchain technology's services inside smart cities, it is necessary to identify the technology's features, as well as its main needs and research obstacles. As such, this essay will try to define the properties of blockchain technology. Additionally, the critical needs for integrating blockchain technology into smart cities are outlined. A possible use case for blockchain technology is used to show how a smart city could be protected. There is also an overview of how a real-world three-blockchain-based smart city case study works. Finally, a number of critical research issues are highlighted and explored.
Framework for Intelligent Smart City Deployment via Artificial Intelligence Software Networking

To take advantage of smart city projects and reap the advantages they promise, the government, educational institutions, non-governmental organizations (NGOs), and private businesses are all focusing their attention on them. There are a lot of people involved in smart city programs, and each of them has a unique set of demands that need to be addressed. For creating and delivering smart city initiatives, this paper examines and analyses the effects and consequences of adopting software-defined networking and artificial intelligence. The bulk of smart city initiatives consider big data to be an essential component. Software-defined networking and machine learning have a significant impact on innovation, and a smart city use case illustrates the potential benefits of cognitive learning for smart cities. Efforts to make cities more efficient and intelligent may need new and imaginative methods of doing things, it is said.