Reviewerly/exHarmony huggingface dataset:
A Survey on Siamese Network: Methodologies, Applications, and Opportunities

Siamese network has obtained growing attention in real-life applications. In this survey, we present an comprehensive review on Siamese network from the aspects of methodologies, applications, and interesting topics for further exploration. We first introduce framework designs of Siamese network, followed by methodologies about learning with unlabeled data. Then, we review application scenarios in terms of classification and regression, together with relative methodologies. We also discuss the promising area of few-shot learning, followed by interesting topics about opportunities and challenges.
Robust twin-field quantum key distribution through sending or not sending

The sending-or-not-sending (SNS) protocol is one of the most major variants of the twin-field (TF) quantum key distribution (QKD) protocol and has been realized in a 511-km field fiber, the farthest field experiment to date. In practice, however, all decoy-state methods have unavoidable source errors, and the source errors may be non-random, which compromises the security condition of the existing TF-QKD protocols. In this study, we present a general approach for efficiently calculating the SNS protocol's secure key rate with source errors, by establishing the equivalent protocols through virtual attenuation and the tagged model. This makes the first result for TF QKD in practice where source intensity cannot be controlled exactly. Our method can be combined with the two-way classical communication method such as active odd-parity pairing to further improve the key rate. The numerical results show that if the intensity error is within a few percent, the key rate and secure distance only decrease marginally. The key rate of the recent SNS experiment in the 511-km field fiber is still positive using our method presented here, even if there is a [Formula: see text] intensity fluctuation. This shows that the SNS protocol is robust against source errors.
PUFTAP-IoT: PUF-Based Three-Factor Authentication Protocol in IoT Environment Focused on Sensing Devices

In IoT-based environments, smart services can be provided to users under various environments, such as smart homes, smart factories, smart cities, smart transportation, and healthcare, by utilizing sensing devices. Nevertheless, a series of security problems may arise because of the nature of the wireless channel in the Wireless Sensor Network (WSN) for utilizing IoT services. Authentication and key agreements are essential elements for providing secure services in WSNs. Accordingly, two-factor and three-factor-based authentication protocol research is being actively conducted. However, IoT service users can be vulnerable to ID/password pair guessing attacks by setting easy-to-remember identities and passwords. In addition, sensors and sensing devices deployed in IoT environments are vulnerable to capture attacks. To address this issue, in this paper, we analyze the protocols of Chunka et al., Amintoosi et al., and Hajian et al. and describe their security vulnerabilities. Moreover, this paper introduces PUF and honey list techniques with three-factor authentication to design protocols resistant to ID/password pair guessing, brute-force, and capture attacks. Accordingly, we introduce PUFTAP-IoT, which can provide secure services in the IoT environment. To prove the security of PUFTAP-IoT, we perform formal analyses through Burrows Abadi Needham (BAN) logic, Real-Or-Random (ROR) model, and scyther simulation tools. In addition, we demonstrate the efficiency of the protocol compared with other authentication protocols in terms of security, computational cost, and communication cost, showing that it can provide secure services in IoT environments.
Developing an industry 4.0 readiness model using fuzzy cognitive maps approach

Industry 4.0, or the fourth industrial revolution, is a new paradigm in manufacturing digitalization, which provides various opportunities for enterprises. Industry 4.0 readiness models are worthy methods to aid manufacturing organizations in tracking the development of their businesses and operations. Nevertheless, there are different Industry 4.0 readiness models; no work has yet analyzed Industry 4.0 readiness degree and causal effects relationships using fuzzy cognitive maps. This paper proposes an Industry 4.0 readiness model that consists of readiness requirements obtained from the literature and validated through a mixed-method approach, including literature reviews and questionnaires. To validate the proposed Industry 4.0 readiness model, the exploratory factor analysis and confirmatory factor analysis methods are used. Fuzzy Cognitive Map is utilized to assess readiness, identify relevant concepts to improve readiness degree, implement Industry 4.0, and analyze causal relationships among concepts and dimensions. Through this model and the FCM method, managers can recognize relevant concepts and predict complicated cause-effect relationships among concepts in two states of static and dynamic analyses to increase readiness degree. The paper concludes by emphasizing managerial implications for successful applications in practice as well as future research suggestions on developing the Industry 4.0 readiness model.
Automatic Text Summarization of Biomedical Text Data: A Systematic Review

In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5% of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.
Rage Against the Machine: Experimental Insights into Customers’ Negative Emotional Responses, Attributions of Responsibility, and Coping Strategies in Artificial Intelligence–Based Service Failures

In their interactions with chatbots, consumers often encounter technology failures that evoke negative emotions, such as anger and frustration. To clarify the effects of such encounters, this article addresses how service failures involving artificial intelligence–based chatbots affect customers’ emotions, attributions of responsibility, and coping strategies. In addition to comparing the outcomes of a service failure involving a human agent versus a chatbot (Study 1), the research framework integrates the potential influences of anthropomorphic visual cues and intentionality (Studies 2 and 3). Through three experimental designs, the study reveals that when interacting with chatbots, customers blame the company more for the negative outcome, experiencing mainly frustration, compared with when they interact with a human agent. As the chatbot is perceived as not having intentions and control over them, it is not considered responsible. Thus, the company bears more responsibility for the poor service performance. However, the authors suggest that anthropomorphic visual cues might help mitigate the negative attributions to the company. The attribution of humanlike characteristics also helps promote both problem-focused coping, which helps consumers actively handle the service failure, and emotion-focused coping, which helps restore the emotional balance disrupted by the negative event.
Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825).
Blockchain and Secure Element, a Hybrid Approach for Secure Energy Smart Meter Gateways

This paper presents a new hybrid approach that is suitable for application to energy smart meter gateways, based on combining both blockchain and Secure Element (SE) technologies serving the roles of a secure distributed data storage system and an essential component for building a "root of trust" in IoT platforms simultaneously. Blockchain technology alone may not completely secure a transaction because it only guarantees data immutability, while in most cases, the data has to be also secured at the point of generation. The proposed combinational approach aims to build a robust root of trust by introducing the SE, which will provide IoT devices with trusted computed resources. The feasibility of the proposed method is validated by testing three different implementation scenarios, using different Secure Element systems (SES) combined with blockchain and LPWAN communication technologies to encrypt, transmit, and save data. This hybrid approach aids in overcoming the obstructions of using any one technology alone, and its use is demonstrated with a case study for an Energy Smart Metering gateway that enables the implementation of a local Peer to Peer energy trading scheme that is end-to-end secure and decentralized.
Object Detection for Construction Waste Based on an Improved YOLOv5 Model

An object detection method based on an improved YOLOv5 model was proposed to enhance the accuracy of sorting construction waste. A construction waste image sample set was established by collecting construction waste images on site. These construction waste images were preprocessed using the random brightness method. A YOLOv5 object detection model was improved in terms of the convolutional block attention module (CBAM), simplified SPPF (SimSPPF) and multi-scale detection. Then, the improved YOLOv5 model was trained, validated and tested using the established construction waste image dataset and compared with other conventional models such as Faster-RCNN, YOLOv3, YOLOv4, and YOLOv7. The results show that: based on the improved YOLOv5 model, the mean average precision (mAP) on the test dataset can reach 0.9480. The overall performance of this model is better than that of other conventional models in object detection, which verifies the accuracy and availability of the proposed method.
Identification of innovative technology enablers and drone technology determinants adoption: a graph theory matrix analysis framework

Two main things ensure technology is integrated into a system: enablers, which help technology get in; and determinants, which act as filters to ensure that the right technology gets in. The study develops a decision framework for ranking determinants of drone technology adoption from the perspective of enablers related to innovative technology integration. This study is based on qualitative expert opinion and quantitative integrated MCDM methods. It provides a valuable graph theory matrix analysis framework for managerial perspectives. Several insights are given into the areas of what makes it possible to use new technologies and what makes people adopt drone technology. The study also points out problems and places where more research is needed.
Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

Solar radiation's intermittent and fluctuating nature poses severe limitations for most applications. Accurate prediction of solar radiation is an essential factor in predicting the output power of a photovoltaic power system. For this purpose, the potential of the 20 MW solar photovoltaic power plant in Zahedan city has been evaluated in this article. With the help of monthly data (1984–2021) and MLP, LSTM, GRU, CNN, and CNN-LSTM models, solar radiation and temperature are predicted for the next ten years. CNN exhibits the best performance compared to other models with four input parameters: global horizontal irradiance, temperature, surface pressure, relative humidity (RH), and two outputs of temperature and radiation. The root mean square error values for global horizontal irradiance and temperature were 12.68 W/m2 and 1.75 °C, respectively. Relative humidity exhibited more significant effect on the model in comparison with surface pressure. Finally, the average annual power output for ten years from 2022 to 2031 is calculated and predicted to be 50.37 GWh.
QAOA-in-QAOA: Solving Large-Scale MaxCut Problems on Small Quantum Machines

The design of fast algorithms for combinatorial optimization greatly contributes to a plethora of domains such as logistics, finance, and chemistry. Quantum approximate optimization algorithms (QAOAs), which utilize the power of quantum machines and inherit the spirit of adiabatic evolution, are approaches to tackle combinatorial problems with potential runtime speedups. However, hurdled by the limited quantum resources nowadays, QAOAs are infeasible to manipulate large-scale problems. To address this issue, here we revisit the MaxCut problem via the divide-and-conquer heuristic: seek the solutions of subgraphs in parallel and then merge these solutions to obtain the global solution. Because of the ${\mathbb{Z}}_{2}$ symmetry in MaxCut, we prove that the merging process can be further cast into a new MaxCut problem and thus be addressed by QAOAs or other MaxCut solvers. In view of this, we propose QAOA-in-QAOA (${\mathrm{QAOA}}^{2}$) to solve arbitrary large-scale MaxCut problems using small quantum machines. We also prove that the performance of ${\mathrm{QAOA}}^{2}$ is lower bounded with respect to the divide-and-conquer process. Experiment results illustrate that, under different graph settings, ${\mathrm{QAOA}}^{2}$ attains a competitive or even better performance over classical algorithms when the node count is around $2000$. Our method can be seamlessly embedded into other advanced strategies to enhance the capability of QAOAs in large-scale combinatorial optimization problems.
Fifteen Years of Recommender Systems Research in Higher Education: Current Trends and Future Direction

Artificial intelligence applications have revolutionized business, industry, and education. Applications developed for other sectors are being adapted for education. The amount of data being generated by educational institutions is used to make recommendations for a wide range of areas. The study uses bibliometric analysis to evaluate the development and growth of recommender systems in higher education. Two hundred seventy-two (272) articles published between 2007 and 2021 in the Scopus database were utilized to collect data. The study examines the different patterns such as publication trends, the relative growth rate (RGR) and doubling time (DT) for publications. Results show a steady increase in publications, with 2017 recording the highest number of publications. China, Spain and the United States have the highest publications. The mean RGR decreased, and the mean doubling time increased in the three-five-year periods. The study identified sixteen themes covered by the research articles analyzed. Most articles focused on e-learning, followed by classroom activities and, thirdly, those focusing on course selection. The study provides essential insights into current and future research on recommender systems in higher education. This analysis helps researchers, policymakers, and practitioners better understand the development of recommender systems in higher education and possible practice implications.
Effect of Competent Human Capital, Strategic Flexibility and Turbulent Environment on Sustainable Performance of SMEs in Manufacturing Industries in Palestine

The contribution of Small and Medium-Sized Enterprises (SMEs) to economic prosperity and expansion is widely acknowledged worldwide. However, the empirical evidence in the literature on enhancing sustainable performance in manufacturing industries, specifically SMEs in developing countries living under constant unstable economic and political environments such as Palestine, is still insufficient. Referring to the theory of Triple Bottom Line (TBL), Contingency Theory (CT), Natural Resource Based View (NRBV) and the relevant literature on sustainable performance in business firms, this study aims to explore the effect of competent human capital (CHC), strategic flexibility (SF) and turbulent environment (TE) on the sustainable performance (SP) of SMEs industries in the context of Palestine. Based on 380 random samples in manufacturing SME industries in the country, 245 useful questionnaires were analyzed using Partial Least Squares Structural Equation Modeling for analysis by the Smart PLS 4.0 software. The findings indicate that several enablers could improve the performance of SMEs in Palestinian industries in terms of sustainability. One of these aspects is adopting a supportive governmental policy to strengthen the sustainability concept in the industrial sector and encouraging industries to move toward circularity and greening their operations. Nevertheless, SMEs need to implement proper managerial tools at the firm’s level that enhance sustainable performance, including investment in competent human capital, developing flexible strategies and proper analysis of the surrounding turbulent environment, since all of these will positively impact and improve the sustainable performance of those SMEs. The contribution of this study to the literature develops current knowledge of sustainable performance in SMEs in manufacturing industries, specifically in countries that suffer from political and economic turbulence, by providing a conceptual framework focusing on managerial and business aspects that contributes to achieving desired sustainable performance. Consequently, the study’s findings also show how CHC, SF and TE are essential managerial tools for organizations and SMEs operating in an extremely competitive and turbulent market.
Decentralised Finance’s timocratic governance: The distribution and exercise of tokenised voting rights

Ethereum’s public distributed ledger can issue tokenised voting rights that are tradable on crypto-asset exchanges by potentially anyone. Ethereum thus enables global, unincorporated associations to conduct governance experiments. Such experiments are crucial to Decentralised Finance (DeFi). DeFi is a nascent field of unlicensed, unregulated, and non-custodial financial services that utilise public distributed ledgers and crypto-assets rather than corporate structures and sovereign currencies. The inaugural Bloomberg Galaxy DeFi Index, launched in August 2021, included nine Ethereum-based projects – non-custodial exchanges as well as lending and derivatives platforms. Each project is governed, at least in part, by unregistered holders of tokenised voting rights (also known as governance tokens). Token-holders typically vote for or against coders’ improvement proposals that pertain to anything from the allocation of treasury funds to a collateral’s risk parameters. DeFi’s governance thus depends on the distribution and exercise of tokenised voting rights. Since archetypal DeFi projects are not managed by companies or public institutions, not much is known about DeFi’s governance. Regulators and law-makers from the United States recently asked if DeFi’s governance entails a new class of “shadowy” elites. In response, we conducted an exploratory multiple-case study that focused on the tokenised voting rights issued by the nine projects from Bloomberg’s inaugural Galaxy DeFi index. Our mixed methods approach drew on Ethereum-based data about the distribution, trading, staking, and delegation of voting rights tokens, as well as project documentation and archival records. We discovered that DeFi projects’ voting rights are highly concentrated, and the exercise of these rights is very low. Our theoretical contribution is a philosophical intervention: minority rule, not “democracy”, is the probable outcome of token-tradable voting rights and a lack of applicable anti-concentration laws. We interpret DeFi’s minority rule as timocratic.
Friend, mentor, lover: does chatbot engagement lead to psychological dependence?

Purpose The purpose of this study is to explore customer-artificial intelligence (AI) service technology engagement and relationship development drivers, as well as potential negative consequences in the context of social chatbots. Design/methodology/approach A sequential mixed-method approach combined exploratory qualitative and confirmatory quantitative analyses. A conceptual model developed from Study 1 qualitative content analysis of in-depth interviews with active users of the AI social chatbot Replika was tested in Study 2 by analyzing survey data obtained from current Replika users. Findings Loneliness, trust and chatbot personification drive consumer engagement with social chatbots, which fosters relationship development and has the potential to cause chatbot psychological dependence. Attachment to a social chatbot intensifies the positive role of engagement in relationship development with the chatbot. Originality/value This study was the first to combine qualitative and quantitative approaches to explore drivers, boundary conditions and consequences of relationship and dependence formation with social chatbots. The authors proposed and empirically tested a novel theoretical model that revealed an engagement-based mechanism of relationship and dependence formation with social chatbots.
Exploring the Metaverse

The definition of the metaverse, a virtual space where people can interact with each other and computer-generated environments, is gaining traction. With the help of artificial intelligence (AI) and virtual reality (VR) technologies, the metaverse is being transformed into a highly immersive and interactive experience. This chapter explores the potential of the metaverse as a new form of tourism. By examining current trends in the development of AI and VR technologies, the authors explore the possibilities of creating highly realistic and personalized travel experiences that can be accessed from anywhere in the world. They also discuss the potential and benefits of the metaverse as a new tourism industry and the concerns that must be addressed in its development. Ultimately, this chapter argues that the metaverse has the potential to revolutionize the tourism industry by offering innovative and immersive experiences that will attract a new generation of travellers.