【Abstract】Lithium -ion battery (LIB) related companies can employ blockchain to record data used to assess the health status of LIBs and thus recycle decommissioned LIBs in an environmentally friendly and economic manner. However, whether blockchain adoption is helpful for the environment and company profitability remains controversial. To this end, we consider a stylized closed -loop supply chain consisting of a LIB supplier (LS), an outsourced-mode EV manufacturer (OM), and an integrated -mode EV manufacturer (IM), and analyze whether IM and LS should adopt blockchain for LIB production and recycling from the perspective of environmental impact and company profitability. First, we show brand preference, the effect of blockchain operational cost disadvantage, and the effect of echelon utilization benefit advantage are the main factors affecting the equilibrium decisions. Second, we show blockchain adoption is viable to mitigate the environmental impact of LIB production and recycling, provided that the energy savings from the echelon utilization method exceed a certain value. Third, we show that both IM and LS will adopt blockchain when the blockchain operating cost is low, whereas neither of them will adopt blockchain technology when the blockchain operating cost is high. In cases where blockchain operating cost is medium, only IM will adopt blockchain.
【Abstract】Tracking and tracing devices can provide real-time information from the supply chain and enable digital transformation in the logistics and supply chain industry. In this connection, Logistics 4.0 refers to the potential for information technology and smart connected assets to be used in logistics in the same way as the Industry 4.0 concept which has been applied in operations and manufacturing. This paper analyses current tracking and tracing-focused applications that can provide value for logistics operations through a case study approach. This study employs an exploratory multiple-case study approach, which is based on interviews with development project stakeholders. The paper analyses three industrial case studies and how tracking/tracing applications are connected to value processes. The findings of this study show that the value of Logistics 4.0, through the deployment of tracking/tracing applications, is delivered in terms of operational efficiency, visibility, transparency, and safety/security. The payback depends on the volume of transactions, the possibility of reusing the tracking tags, the duration of trips, and the supply chain structure. The paper provides insight into how Logistics 4.0 technology can enhance logistics performance value. Based on the analysis, the study proposes the following potential application domains: (1) intermodal tracking of the shipments for operations control, (2) asset management of containers, and (3) certification of the process steps and authenticity.
【Abstract】Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.
【Abstract】With live streaming becoming an integral part of a seller's business strategy, a retailer can choose an influencer from a multi-channel network (MCN) to attract consumers. However, since the MCN may benefit from exaggerating the influencer's historical live sales data, information asymmetry between the retailer and the MCN exists. This may lead the retailer to misestimate the influencer's demand-enhancing capability (DEC), and then suffer loss. As the usage of blockchain in providing trust becomes more common, an MCN can apply blockchain technology to signal its data reliably to a retailer. However, conditions for MCN to adopt blockchain and its impact on retailer's influencers choice has not been investigated so far. To fill this gap, we built a signaling model in which an MCN knows the actual information reliability whereas the retailer does not. We find that the retailer opens a live-streaming channel only if the operating cost is low but the retailer will use its own staff in the live streaming rather than an influencer from MCN if the commission is high. This makes the MCN to refrain from adopting blockchain technology. Meanwhile, the blockchain adoption decisions of the MCN and its impact on the retailer's influencers choice not only depend on the adoption cost but also the commission. Specifically, when the adoption cost is low, a high-reliability MCN benefits more from adopting blockchain technology, which incentivizes the retailer to choose a highDEC influencer; as the adoption cost increases, retailers become unable to judge the MCN's information reliability, especially when the commission level is too low or too high; when the adoption cost increases to a moderate range, multiple equilibria co-exist, indicating that an MCN's willingness to adopt blockchain technology depends on the retailer's belief. We find that all equilibria survive the intuitive criterion, but, from both types of MCN's perspectives, the equilibrium without blockchain adoption is always Pareto-dominant. When the adoption cost is high, an MCN has no incentive to adopt blockchain technology.
【Abstract】According to research, the vast majority of road accidents (90%) are the result of human error, with only a small percentage (2%) being caused by malfunctions in the vehicle. Smart vehicles have gained significant attention as potential solutions to address such issues. In the future of transportation, travel comfort and road safety will be ensured while also offering several value-added services. The automotive industry has undergone a significant transformation through the use of emerging technologies and wireless communication channels, resulting in vehicles becoming more interconnected, intelligent, and safe. However, these technologies and communication systems are susceptible to numerous security attacks. The objective of this paper is to present a comprehensive overview of the smart vehicle's architecture, encompassing emerging technologies and security challenges and solutions associated with smart vehicles. There has been a significant surge in the utilization of machine learning techniques in smart vehicles. We categorically discuss common security measures, including machine learning and deep learning based solutions that have been mentioned in the literature and implemented against security threats on smart vehicles. This paper has also been titled a tutorial due to its layout, which begins with covering preliminary knowledge, terminologies, and encompassing technologies required to comprehend smart vehicles. Following this, the paper addresses the overall challenges associated with smart vehicles and then focuses on security issues. In terms of solutions, the paper discusses overall solutions to security issues in smart vehicles before delving into a specific solution based on machine learning and deep learning.
【Abstract】Recent advancements in Vehicle-to-Grid (V2G) lead to efficient service provisions, such as eco-friendly environment, demand response management, charging, and discharging to the end-users. However, security and privacy preservation for the aforementioned services are key challenges keeping in view of the dependency on the existing centralized security architectures which are not resilient to fault tolerance due to a single point of failure. Hence, there is a need to design new efficient security solutions for the current V2G network, so as to provide seamless services to the end-users. Motivated by these, in this work, we proposed a bloom filter-enabled smart contract-based scheme for access control in V2G environment. In comparison to complex signature-based cryptographic techniques, we propose bloom filter-based authentication for the registered nodes for efficient storage and searching of stored data on the blockchain network. We also designed the Proof-of-Authority (PoA) consensus mechanism, which selects authority nodes dynamically to verify various transactions on the blockchain network. To validate the proposal, we implemented it on the Ethereum network on benchmark datasets using various evaluation parameters such as-latency, throughput, false positive probability, and gas cost.
【Abstract】We investigate the impact of rival entry on blockchain technology development by modelling a supply chain, in which an incumbent manufacturer (she), facing the entry of an entrant manufacturer (it), develops the blockchain platform through investments to improve the traceability and transparency of products procured from one supplier (he). Our study shows the rival entry (competition) may not necessarily promote blockchain technology adopted by the incumbent manufacturer, whether the blockchain platform is permissioned or non-permissioned. Furthermore, the entry of a new firm may trigger an 'all-win' situation for all parties involved not only in the permissioned scenario but, surprisingly, also in the non-permissioned one. In such a situation, the incumbent manufacturer and the supplier will be both more profitable conditionally, after the entrant manufacturer enters the market. Finally, the issue that a permissioned or non-permissioned blockchain platform should be built is discussed in this work. If the use cost coefficient and the development cost coefficient of blockchain technology are both low, or if the use cost coefficient is high while the development cost coefficient is low (not very low), the incumbent manufacturer should establish a permissioned blockchain platform, and otherwise a non-permissioned one.
【Abstract】The study aims to explore the risk management potential of historical (gold and crude oil) and modern (bitcoin) assets against the shocks in clean energy stocks. Specifically, the quest related to which asset(s)? In what proportion? In which market condition? and for what time preference? should be held with cleaner equity. The exploration is based on the presumptions that conventional and cleaner stocks are alike and that financial markets (assets) have an asymmetric interdependence. We also assume that the interdependence among the considered markets is driven by various events over time and distinct investment horizons. To that end, we employ a generalized vector autoregressive-based overall, time-varying, asymmetric, and frequency-based connectedness measures and DCC-GARCH model to the daily data ranging from October 15, 2014 to April 12, 2023. The spillovers across the considered markets confirm the similarity of conventional and cleaner stocks and the asymmetry hypothesis, suggesting that downside shock transmission (bad spillovers) outperforms upside shock transmission (good spillovers). The time-varying results suggest that connectedness among the considered markets is higher, and asymmetric during Brexit-2016, the COVID-19 pandemic, Russia's invasion of Ukraine in early 2022, and the monetary policy tightening of 2022 and 2023. Finally, we show that gold is a preferable diversifier of cleaner equity risk on average, during distress events, downside markets, and across all investment horizons. The study has implications for financial market participants to strategically manage the cleaner equity risk with gold in diverse market scenarios.
【Abstract】We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.
【Abstract】With the development of 5G/6G communication networks, the industrial Internet of Things (IIoT) industry has generated a massive amount of data, presenting opportunities for advancements in the field of machine learning. The core of machine learning, labeled datasets, requires qualities such as diversity, quantity, and quality. However, the current collection of training datasets is mostly centralized, relying on crowdsourcing systems as platforms. Hence, there are four open challenges existing: (1) traditional crowdsourcing systems are mostly based on centralized platform which often suffers single point of failure, mischief attacks, DDoS attacks and are easy to be remotely hijacked; (2) the transparency of traditional crowdsourcing systems cannot be guaranteed, which results in the unfairness in assigning task or rewarding worker; (3) the quality of labeled training dataset cannot be guaranteed as the workers in traditional crowdsourcing systems are professional or non-professional; (4) the privacy of labels cannot be preserved as the platform can also learn the labeled result. In this paper, we utilize web 3.0 technology to propose a blockchain-based labeled training dataset supply system which can simultaneously supply annotation service and labeled training dataset with the challenges above overcome. Meanwhile we design a privacy-preserved truth discovery suitable for categorical data by combining it with Software Guard Extensions (SGX). Furthermore, we design a fair rewards distribution mechanism which is based on reputation system and Shapley value. The two mechanisms above can ensure the quality of labeled training dataset. Finally, to demonstrate the practicability of our design, we implement a prototype of the system deployed on Fabric test network and conduct extensive simulations. Compared to applications on public chains, the throughput of our blockchain application can reach 53 times higher.
【Abstract】In this paper, we utilise blockchain technology (BT) and circular q-rung orthopair fuzzy sets ( Cq-ROFS) to address practical issues related to urban transportation and supply chain management (SCM). Recognising the weaknesses of earlier approaches such as circular intuitionistic fuzzy sets (C-IFS), we work Cq-ROFS to better accommodate imprecise input. Novel approaches that use into metaverse settings are being investigated as a means of addressing the complex problems associated with urban mobility. This study use the SWARAAROMAN approach to evaluate potential blockchain integration possibilities for metaverse urban mobility. Sensitivity analysis and comprehensive evaluation yield powerful insights into the robustness and adaptability of solutions. With these findings at their disposal, policymakers will be more equipped to take on unpredictability and take advantage of opportunities for sustainable urban mobility. To enhance urban transportation solutions in the dynamic metaverse, future strategies should concentrate on improving processes and exploring novel technology. Ultimately, this research emphasises how critical it is to foster interdisciplinary collaboration and ongoing innovation if we hope to influence the patterns of urban mobility in the metaverse.
【Abstract】In this bibliometric study, the significant transformations in the financial sector brought about by automation and technological advancements from 1984 to 2022 are explored. A total of 863 articles is analyzed, and a consistent upward trajectory in research focused on fast trading technologies and algorithmic strategies is identified. The key findings reveal that the research is grouped into five thematic clusters, ranging from algorithmic trading and machine learning to systemic risks associated with high-frequency trading and the impacts of algorithmic trading on market quality. This study encapsulates the evolving landscape of financial markets, emphasizing emerging trends in cryptocurrencies and machine learning, which will continue to shape future research directions. In conclusion, five macroareas and ten specific future research areas are proposed.
【Abstract】This paper takes part in the ongoing debate on the newly emerging field of financial technology by systematically reviewing 164 articles on cryptocurrency volatility during the period from 2016 to December 2022. This paper also aims to enlighten academics and practitioners about the beneficial insights gained from cryptocurrency volatility research, identify existing research gaps, and propose a new research agenda in the subject of study. To this end, realized volatility, almost all stylized facts, implied volatility, stochastic volatility, and drivers of volatility are discussed. Finally, we propose that future researchers concentrate on high-frequency data (i.e., hourly, minutely, and secondly), the use of machine learning models, crypto derivatives, crypto individual investor behavior, the impact of the new existence of institutional investors, stablecoins, and the evaluation of the forecasts of cryptocurrency volatility.
【Abstract】The Industrial Internet of Things (IIoT) has introduced digitalization and intelligence to the industrial field. However, the terminal in IIoT has a low computing power and limited storage resources, thus it is necessary to calculate and unload the data generated by the terminal and to allocate resources. Aiming to resolve the issues of computation offloading and resource allocation in IIoT, a blockchain-assisted data communication and computing service strategy of IIoT was constructed in this study. First, a blockchain-assisted computing and communication resource allocation model in IIoT was established, which embeds resource allocation and computation offloading into a block-chain to ensure the credibility of data during the process. In order to find the minimum value of the weighted sum of system energy cost, as well as the delay in blockchain-assisted resource allocation and computation offloading, a Levy flight-enhanced JAYA optimization algorithm(p-JAVA) based on blockchain resource allocation was proposed, which was embedded into the blockchain system to ensure the security of the data when allocating resources. Simultaneously, Levy flight was used to modify the algorithm to improve the convergence speed of the algorithm. The simulation results demonstrated that the Levy flight-enhanced JAYA optimization algorithm consumed 21% of the energy compared with the genetic algorithm, and 17.7% energy compared with the particle swarm optimization algorithm. The load balancing ability of the Levy flight-enhanced JAYA optimization algorithm is relative to the particle swarm optimization and genetic algorithms, which significantly increases the energy and delay consumption in the resource allocation of IIoT and improves the system performance.
【Abstract】With the rapid development of smart grid, constructing distributed energy trading market (DETM) based on blockchain to coordinate distributed energy resources (DER) has become a future direction. However, existing consensus algorithms of blockchain face many challenges in large-scale energy trading scenarios, such as high resource overhead, slow transaction procedure. To solve the above crucial problems for wide deployment of distributed energy trading, this study proposes a novel consensus algorithm named Lightweight adaptive Byzantine fault tolerant consensus (LA-BFT), and a reputation calculation method based on behavioral characteristics for selection of consensus nodes. The LA-BFT consists of two parts: (i) weak consensus for normal cases. By introducing threshold signature mechanism, weak consensus simplifies the consensus process to achieve linear communication complexity O ( n ) . (ii) byzantine node detection scheme is enable for malicious cases. With consensus committee, the detection scheme can detect the potential byzantine nodes by crossvalidation, which ensures transaction safety. The reputation calculation method is presented to cooperate with LA-BFT to elect leaders and candidates for consensus procedure. Once a round of consensus is completed, the reputation of each node needs to be updated, only nodes with high reputation are eligible to become leaders or committee nodes in the next round. With the reputation calculation method, honest nodes and byzantine nodes can be effectively identified, ensuring the security of the consensus process. Numerical results indicate that LA-BFT exhibits superior performance on communication overhead and bandwidth occupancy in largescale concurrent energy trading scenarios. When the number of nodes is 50, under normal scenarios, LA-BFT's communication overhead is notably lower, constituting a mere 6.02% of PBFT and 24.26% of SHBFT, while bandwidth occupancy amounts to merely 5.55% of PBFT and 9.76% of SHBFT.
【Abstract】The Social Internet of Things (Social IoT) introduces a fresh approach to promote the usability of IoT networks and enhance service discovery by incorporating social contexts. However, this approach encounters various challenges that impact its performance and reliability. One of the most prominent challenges is trust, specifically trust-related attacks, where certain users engage in malicious behaviors and launch attacks to spread harmful services. To ensure a trustworthy experience for end-users and prevent such attacks in realtime, it is highly significant to incorporate a trust management mechanism within the Social IoT network. To address this challenge, we propose a novel trust management mechanism that leverages blockchain technology. By integrating this technology, we aim to prevent trust-related attacks and create a secure environment. Additionally, we introduce a new consensus protocol for the blockchain called Spark-based Proof of Trustrelated Attacks (SPoTA). This protocol is designed to process stream transactions in real-time using Apache Spark, a distributed stream processing engine. To implement SPoTA, we have developed a new classifier utilizing Spark Libraries. This classifier is capable of accurately categorizing transactions as either malicious or secure. As new transaction streams are read, the classifier is employed to classify and assign a label to each stream. This label assists the SPoTA protocol in making informed decisions regarding the validation or rejection of transactions. Our research findings demonstrate the effectiveness of our classifier in predicting malicious transactions, outstripping our previous works and other approaches reported in the literature. Additionally, our new protocol exhibits improved transaction processing times.
【Abstract】With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians' reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft's ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security.
【Abstract】This work proposes a Blockchain-enabled Organ Matching System (BOMS) designed to manage the process of matching, storing, and sharing information. Biological factors are incorporated into matching and the cross-matching process is implemented into the smart contracts. Privacy is guaranteed by using patient-associated blockchain addresses, without transmitting or using patient personal records in the matching process. The matching algorithm implemented as a smart contract is verifiable by any party. Clinical records, process updates, and matching results are also stored on the blockchain, providing tamper-resistance of recipient's records and the recipients' waiting queue. The system also is capable of handling cases in which there is a donor without an immediate compatible recipient. The system is implemented on the Ethereum blockchain and several scenarios were tested. The performance of the proposed system is compared to other existing organ donation systems, and ours outperformed any existing organ matching system built on blockchain. BOMS is tested to ascertain its compatibility with public, private, and consortium blockchain networks, checks for security vulnerabilities and cross-matching efficiency. The implementation codes are available online.
【Abstract】The concept of the Metaverse has sparked great interest as a futuristic virtual space that provides immersive experiences and social interactions through digital avatars. However, using avatars in the Metaverse raises privacy concerns that require innovative solutions to ensure the safety of users. In this article, we conducted a systematic review using the PRISMA method to identify work discussing privacy issues related to avatars in the Metaverse and efforts to provide safer virtual environments for users. Our review revealed two main avatar-related privacy issues: threats related to the user's identity, such as the disclosure of personal details, and social threats, such as harassment. We also reviewed different proposed solutions to these problems, categorized into three main groups: altering user representation, providing safety options to users, and leveraging AI techniques to detect and mitigate issues. While these solutions promise a safer Metaverse for users, there are inherent limitations that require further advancements. To fill the existing research gap and create a safer Metaverse, we suggest improving safety features for users, finding a balance between user experience and privacy, increasing the use of AI to create safer environments while considering user concerns, and enhancing reporting methods available in the Metaverse. Our review emphasizes the need for more advanced research and development to tackle avatar privacy challenges in the Metaverse.
【Abstract】This study examines the investment value of information provided by crypto-influencers, that is, social media influencers covering crypto assets on Twitter. We examine the returns associated with approximately 36,000 tweets issued by 180 of the most prominent crypto social media influencers covering over 1,600 crypto assets for the two years spanning through December 2022. Our primary results indicate that crypto-influencers' tweets are initially associated with positive returns. However, these tweets are followed by significant negative longer-horizon returns, suggesting they generate minimal long-term investment value. These effects are most pronounced for tweets issued by crypto-influencers proclaiming to be crypto experts, for smaller cap crypto asset securities and for self-described experts with many Twitter followers. In an additional analysis, we use machine-learning methods to classify tweets and find that this pattern of results strengthens when the tweets have a more positive sentiment or relate to buy recommendations.
【Abstract】This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. MADL places appropriate emphasis not only on the quality of the point forecast but also on its impact on the rate of achievement by the investment system based on it. The introduction and detailed description of the theoretical properties of this new MADL loss function are our main contributions to the literature. In the empirical part of the study, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that our new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.
【Abstract】Payment channel networks (PCNs) have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. It provides a high-throughput solution for blockchain-based payment systems. However, such "layer-2" blockchain solutions have their own problems: payment channels require a separate deposit for each channel of two users. Thus it significantly locks funds from users into particular channels without the flexibility of moving these funds across channels. In this paper, we proposed Aggregated Payment Channel Network (APCN), in which flexible funds are used as a per-user basis instead of a per-channel basis. To prevent users from misbehaving such as double-spending, APCN includes mechanisms that make use of hardware trusted execution environments (TEEs) to control funds, balances, and payments. The distributed routing protocol in APCN also addresses the congestion problem to further improve resource utilization. Our prototype implementation and simulation results show that APCN achieves significant improvements on transaction success ratio with low routing latency, compared to even the most advanced PCN routing.
【Abstract】Cryptocurrency is a digital asset secured by cryptography that has become a popular medium of exchange and investment known for its anonymous transactions, unregulated markets, and volatile prices. Given the popular subculture of traders it has created, and its implications for financial markets and monetary policy, scholars have recently begun to examine the political, psychological, and social characteristics of cryptocurrency investors. A review of the existing literature suggests that cryptocurrency owners may possess higher-than-average levels of nonnormative psychological traits and exhibit a range of non-mainstream political identities. However, this extant literature typically employs small nonrepresentative samples of respondents and examines only a small number of independent variables in each given study. This presents the opportunity for both further testing of previous findings as well as broader exploratory analyses including more expansive descriptive investigations of cryptocurrency owners. To that end, we polled 2,001 American adults in 2022 to examine the associations between cryptocurrency ownership and individual level political, psychological, and social characteristics. Analyses revealed that 30% of our sample have owned some form of cryptocurrency and that these individuals exhibit a diversity of political allegiances and identities. We also found that crypto ownership was associated with belief in conspiracy theories, "dark" personality characteristics (e.g., the "Dark Tetrad" of narcissism, Machiavellianism, psychopathy, and sadism), and more frequent use of alternative and fringe social media platforms. When examining a more comprehensive multivariate model, the variables that most strongly predict cryptocurrency ownership are being male, relying on alternative/fringe social media as one's primary news source, argumentativeness, and an aversion to authoritarianism. These findings highlight numerous avenues for future research into the people who buy and trade cryptocurrencies and speak to broader global trends in anti-establishment attitudes and nonnormative behaviors.
【Abstract】The primitive of vector commitment scheme allows a user to commit to an ordered sequence of messages (i.e., a vector) and later open the commitment at any position subset of the vector. The most important and desirable feature of vector commitment schemes is that the size of the opening proof is sublinear in the length of the committed vector. The original vector commitment scheme has now been extended to support several new functionalities like aggregation, updatability and homomorphism, and has applications ranging from verifiable data streaming to stateless cryptocurrency. Among these extensions, the linear-map vector commitment (LVC) scheme enables a user to open a general linear map evaluated on the committed vector, rather than those messages of the committed vector as in the original vector commitment scheme. However, the existing LVC schemes are only proved to be secure under the idealized assumptions, i.e., using the algebraic group model, which might be unpractical in the real world. To this end, we eliminate the use of algebraic group model, and propose a practically secure LVC construction. Our construction achieves practical security by additionally generating degree proofs for polynomials that enable a verifier to check the degree of polynomials publicly. We prove the security of the proposed LVC construction in the standard model under a q-type complexity assumption over bilinear groups. Moreover, we demonstrate how to use the proposed LVC scheme to construct maintainable vector commitments and verifiable data streaming protocols. The theoretical comparison and experimental results indicate that our proposal provides stronger security guarantee, while being competitive in terms of efficiency.
【Abstract】This study introduces a brand-new swarm-inspired algorithm dubbed dholes hunting-based optimization (DhoH) based on an animal hunting strategy to solve global optimization problems. The technique is a brilliant idea for simultaneously finding many local minima. The dhole's hunting strategy is coordinated by members of a swarm, clustering and chasing prey. A clustering approach and finding an optimal global algorithm describe primarily based on gradient approximation. We use four benchmark function datasets to evaluate the DhoH's performance. We compare the achieved results with several previous research from various well-known algorithms. The comparisons demonstrate that DhoH is better than other meta-heuristic algorithms in most cases and determines high-quality solutions with fewer control parameters. Besides, we also explore the application of DhoH in optimizing the decentralized level of Meta-heuristic Proof of Criteria consensus protocol (MPoC) in Blockchain Network to further demonstrate its potential in multi-dimensional problems. The results show the superior effectiveness of DhoH in terms of computational burden and solution precision compared with the existing optimization techniques in the literature.
【Abstract】In order to improve cybersecurity in newly developed network infrastructures, this research investigates the integration of blockchain technology with zero-trust security concepts. The zero-trust paradigm ensures continuous authentication across entities, in contrast to standard security models that often presuppose trust based on a network environment. Blockchain is used to decentralize and impose authentication intensity of communication clarity and honesty. The study compares the performance of the zero trust model enhanced by blockchain to traditional security systems in a number of parameters, such as intrusion detection rates and security breach reaction times, using extensive simulations. The findings demonstrate that the blockchain-enhanced zero-trust architecture performs better than conventional systems in both identifying and countering threats and methodically handling a large volume of transactions when under pressure. These conclusions, which emphasize significant advancements in security applications and system resilience, are predicated on the use of blockchain in zero-trust systems. Subsequent investigations will endeavor to enhance these technologies and investigate their utilization in networks across diverse intricate scenarios.
【Abstract】We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China's financial market, further exploring the impact of cryptocurrencies on China's financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China's financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China's financial market is significantly stronger than the spillover of China's financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China's exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China's financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. The findings of this study have several implications for policymakers and investors.
【Abstract】This study investigates the tail risk transmissions across a diverse range of US commodity & techdriven sector ETFs and the underlying US stock market by employing the CAViaR-based TVP-VAR methodology on daily data from January 01, 2019, to August 17, 2023. Findings reveal that Covid-19 triggered a notable surge in the total connectedness, consequently amplifying the tail risk transmissions within the system. Moreover, the S&P 500, AI&Robotics and fintech sector ETFs stand out as the primary risk transmitters, while cybersecurity and blockchain sector ETFs are risk receivers within the system, except for a notable shift during the peak of the pandemic. The pairwise results reveal limited risk transmissions between the S&P 500, AI&Robotics and fintech sector ETFs; however, both sector ETFs stand out as potential risk transmitters for the VIX index. In contrast to energy, agriculture and base metals sector ETFs, which are persistent risk receivers for both stock market indices and tech-driven sector ETFs, precious metals sector ETFs appear somewhat isolated and therefore offer a potential source of diversification among commodity sector ETFs. In sum, our findings offer valuable sectoral insights for effective risk management and portfolio diversification strategies in dynamic market conditions.
【Abstract】Many enterprises are currently engaged in developing blockchain-based business models. Enterprise networks offer a variety of potential applications for blockchain solutions as they benefit from transparency and security as well as automation of handling data, material, and financial flows along their supply chains. Despite profound potentials, the indicated business models are still in their early stages and need further investigation. To provide an overview of existing blockchain-based business models in the context of enterprise networks, the underlying paper designs a multidimensional taxonomy and identifies several archetypes of blockchain-based businesses. For the taxonomy development, data from 101 blockchain start-ups serves as a basis for empirical validation. Using hierarchical clustering and the k-means method, seven archetypes that sharpen the understanding of how blockchain solutions affect business models in enterprise networks and enable new business models are derived. The proposed work results are intended to be applied in future research and practice to classify and assess the integration of blockchain solutions into existing business models and to support developing new ones that leverage emerging technological capabilities.
【Abstract】We apply a Time-Varying Parameter Vector Auto Regressive (TVP-VAR) connectedness approach on global assets to investigate time-varying dynamic connectedness, portfolio performance, and hedge effectiveness during COVID-19 and the Russia-Ukraine war. With increased connectedness and the changing role of energy and soft commodities during these two events, we find the minimum correlation (connectedness) portfolio performing better during COVID-19 and the Russia-Ukraine war and that cumulative returns of portfolios are higher during COVID-19. Additionally, we find varying (stable) hedge effectiveness of equity market indices and soft commodities (cryptocurrencies). This paper provides specific insights to investors about using optimal portfolios and hedging during pandemics and military conflicts.
【Abstract】Recently, the spotlight has been cast on smart healthcare by many researchers to provide better facilities to patients. Improved services, such as reducing health hazards, monitoring patient health, tracking disease trends, and enhancing service quality, can be offered by smart healthcare. Despite its numerous potential benefits, smart healthcare is associated with some security challenges. These challenges can be mitigated by utilizing blockchain technology, which is characterized by decentralization, cryptography, consensus mechanisms, transparency and accountability, smart contracts, ownership of data, immutability, and distributed ledger. Therefore, the latest blockchain technology is focused in this article to address the security challenges of smart healthcare. In this article, attention is given to smart healthcare, smart cities for smart healthcare, smart and secure healthcare, and cutting-edge technologies for smart cities and smart healthcare.Please provide author biography and photo.
【Abstract】With the rapid development of the logistics industry and the continuous growth of e-commerce, effectively monitoring logistics warehouses has become increasingly important to ensure the security of goods and oversee activities within storage facilities. Although current surveillance systems provide a certain level of security for logistics warehouses, they still face issues such as data tampering, storage, and access management. These challenges can compromise the integrity of surveillance video data, making the system vulnerable to unauthorized access. To address these challenges, this paper proposes the implementation of blockchain-based security management and access control of video data in logistics warehouses. Specifically, the solution employs the Hyperledger Fabric consortium blockchain to execute smart contracts and store the hash values of video data, thereby detecting any tampering and enhancing the security and integrity of the data. Additionally, hybrid encryption technology is utilized to ensure the confidentiality of video data during transmission and storage. Furthermore, the solution leverages the InterPlanetary File System (IPFS) for distributed video storage. This not only increases the redundancy and accessibility of data storage but also reduces the risk of single-point failures. A Role-Based Access Control (RBAC) mechanism is also introduced to strictly manage access permissions to video data, ensuring that only authorized users can access the data, thereby effectively preventing unauthorized access and data breaches. Through a comprehensive analysis of computational and communication costs and the evaluation of blockchain performance at 100 transactions per second for different transaction volumes using Hyperledger Caliper, the results demonstrate the effectiveness and efficiency of the proposed method. Compared to current research, this solution exhibits higher security, providing a new approach for the secure management and access control of video data in logistics warehouses.
【Abstract】The Internet of Things (IoT) represents a network framework comprising identifiable entities that interact through the Internet. One of its applications is the smart home, where household devices can be remotely monitored and controlled. This has led to an increased demand for reliable security solutions in IoT systems. Security presents a significant challenge in IoT smart home devices and must be carefully considered. Unauthorized access to a smart home system, facilitated by means such as jamming or replay attacks, could pose risks by manipulating sensors and controls, potentially allowing unauthorized entry. This review paper concentrates specifically on the security and privacy aspects of IoT smart home access control devices. It begins with a concise overview of smart home security and privacy, then delves into various techniques within the smart home system taxonomy, such as authentication, access control, blockchain, and cryptography-based methods. Furthermore, the paper compares the advantages and disadvantages of these techniques. It also examines various types of attacks on smart home IoT access control systems and evaluates risk factors such as methodologies, attack frequency, severity, probability, and ranking. Finally, the paper discusses challenges, applications, conclusions, and future directions.
【Abstract】The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. $ FRM@Crypto $ FRM@Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.
【Abstract】This paper investigates the dynamic interplay between Environmental, Social, and Governance (ESG) principles and Financial Technology (FinTech) innovations within Green finance. Drawing insights from real-world case studies, including Eneco's green bond issuance, Wealthsimple's robo-advisory platform, IBM Food Trust's blockchain-based supply chain finance, and Abundance Investment's P2P lending for renewable energy, the study explores how organizations are integrating ESG considerations with FinTech solutions across diverse sectors. The findings reveal sector-specific impacts, commonalities, and challenges, emphasizing the potential for enhanced transparency, responsible investment practices, and positive societal outcomes. The study contributes to the evolving discourse on sustainable finance by offering actionable recommendations for practitioners, policymakers, and researchers seeking to navigate the complex convergence of ESG and FinTech for a more sustainable financial ecosystem.
【Abstract】This study employs the cross-sectional absolute deviation model and Carhart pricing model to examine the existence and authenticity of various market sizes and liquidity levels within cryptocurrency markets. Additionally, we introduce a herding effect measurement index tailored for the cryptocurrency market and predict cryptocurrency prices by integrating the long short-term memory (LSTM) neural network model. Empirical results reveal the presence of both genuine and pseudo herding phenomena in cryptocurrency markets, with information acquisition asymmetry identified as a significant driver of herding behavior. Specifically, during market downturns in the overall market, only pseudo herding is observed in the upward market, whereas during periods of market prosperity, both genuine and pseudo herding are evident in the downward market. In markets of different sizes, herding is absent in cryptocurrency markets with small market value, while in large market value cryptocurrency markets, pseudo herding is not statistically significant. Genuine herding occurs in both upward and downward markets during non-downturn periods. Regarding cryptocurrency markets with different liquidity levels, herding behavior is not observed in markets with small trading volume. Conversely, in markets with large trading volume, pseudo herding is observed in both upward and downward markets during non-downturn periods, with genuine herding occurring in both markets during boom periods. Additionally, the LSTM model demonstrates superior capability in fitting the price trends of different cryptocurrencies, and considering the herding effect index significantly enhances the accuracy of cryptocurrency price prediction.
【Abstract】Similar to traditional supply chain finance (SCF) models, green supply chain finance (GSCF) also faces issues such as information asymmetry and heavy reliance on the creditworthiness of transaction parties. Under the influence of internet ideology, cracking down on traditional GSCF financing issues and transitioning from interpersonal trust to digital trust has become an inevitable trend. Achieving real-time, transparent, correlated, and traceable digital trust, digital technology (DT) platforms provide a solution. Based on the background of "Green Carbon Chain Pass" bill discounting financing business in the GSCF model of "Jian Dan Hui (JDH) platform", game models are constructed involving small and medium-sized enterprises (SMEs), financial institutions (FIs), and core enterprises (CEs) in traditional model and after accessing the platform, based on game theory and considering the uncertainty in the decision-making process. The key factors influencing the strategic choices of the players and the impact mechanism of DT empowering the development of GSCF are explored. MATLAB software is used for simulation experiments. The results show that the cost of business operation, bill maturity values, discount rate, and losses caused by CEs not pay as agreed are important factors affecting the strategic choices of SMEs, FIs, and CEs; Accessing digital platform makes it easier to satisfy the conditions for the tripartite game to evolve into an ideal stable state; Splitting the value of supply chain bills by accessing digital platform can promote business cooperation between FIs and SMEs; The platform, relying on blockchain technology, encourages CEs to pay bills as agreed by increasing default losses; The platform relies on green ratings to motivate SMEs to apply for discounting financing through differentiated financing rates, while promoting their green management; Accessing to digital platform brings efficiency improvements and credit rewards, both of which encourage the three players to choose active financing strategies.
【Abstract】Smart contracts, integral to blockchain technology, automate agreements without intermediaries, ensuring transparency and security across various sectors. However, the immutable nature of blockchain exposes deployed contracts to potential risks if they contain vulnerabilities. Current approaches, including symbolic execution and graph-based machine learning, aim to ensure smart contract security. However, these methods suffer from limitations such as high false positive rates, heavy reliance on trained data, and over-generalization. The goal of this paper is to investigate the application of Wide and Deep Neural Networks in identifying vulnerabilities within smart contracts. We introduce WIDENNET, a method based on deep neural networks, designed to detect reentrancy and timestamp dependence vulnerabilities in smart contracts. Our approach involves extracting bytecodes from the contracts and converting them into Operational Codes (OPCODES), which are then transformed into distinct vector representations. These vectors are subsequently fed into the neural network to extract both complex and simple patterns for vulnerability detection. Testing on real-world datasets yielded an average accuracy of 83.07% and a precision of 83.13%. Our method offers a potential solution to mitigate vulnerabilities in blockchain applications.
【Abstract】Blockchain technology is a groundbreaking and highly secure decentralized digital ledger used to record and store transactions across a network of computers. Its primary purpose is to safeguard, monitor, and oversee digital assets, providing robust protection against unauthorized tampering, revisions, or deletions. It serves as an immutable and tamper-resistant ledger ideal for storing digital evidence, en abling the tracking of evidence' s origins while strictly controlling access to authorized individuals. Current evidence management systems lack essential functionalities, such as authenticating intermediate user access and efficiently transferring evidence access between users. These systems also rely on the Base64 algorithm, which presents challenges related to storage capacity, time delays, scalability, and transaction throughput. To address these limitations, this research introduces an innovative solution: The integration of the Base64 scheme with sharding and the Interplanetary File System (IPFS). This integration is designed to bolster transaction performance, scalability, and throughput. The Base64 scheme plays a pivotal role by encrypting image evidence, securely housing it within the blockchain network. Concurrently, IPFS decentralizes the storage of these images, thereby optimizing memory usage and enhancing transaction throughput within the blockchain environment. Experimental results showcase the efficacy of the proposed SHARD-FEMF, demonstrating a 25% improvement in memory utilization, a 21.5% reduction in gas utilization, and a 23% enhancement in transaction scalability compared to the existing Base64 scheme. Through the combined utilization of sharding and IPFS, the SHARD-FEMF framework represents a significant advancement in efficient forensic evidence management leveraging blockchain technology .
【Abstract】More than 220 enterprises in China's real estate industry have gone bankrupt, causing serious losses. The National Bureau of Statistics of China showed that the country's investment in property development fell by 8.5% year -on -year, while domestic lending dropped by 11.5% and the use of foreign capital fell by 43%. Upon this, the development of supply chain finance can alleviate the pressure on enterprise funds and stabilize the real estate market. However, risk in supply chain finance is the biggest obstacle to the development of supply chain finance and current researches on risk assessment of supply chain finance face problems such as imprecise classification, slow assessment speed, a small number of samples, and data that is easily tampered with. Therefore, this study integrated graph convolutional neural networks into the smart contracts of the contract layer of blockchain. This integration established a novel intelligent perception model for supply chain finance risk. Based on a consortium chain with the government and enterprises as nodes, the model was established, including risk monitoring, assessment, and categorized early warnings. In the risk assessment part, we compared the graph convolutional neural network with multilayer perceptron and support vector machine finding that the accuracy rate of the graphic convolutional neural network is 94%, which is higher than the above models. The intelligent risk -perception model proposed in this paper operates faster than expert judgment assessments used by banks. It also provides accurate risk levels and quantifies the probability of enterprises being classified as high -risk, offering technical support to regulatory authorities in controlling supply chain financial risk.
【摘要】法定数字货币(Central Bank Digital Currency, CBDC)是既有数量又有方向区分的智能“异质矢量”货币,有助于实现货币政策的定向精准调控。基于CBDC的流向主体条件触发机制,构建了货币效用生产(Money in Utility&Production, MIUP)模型,并分别模拟了面向家庭或厂商的定向数量型货币政策调控效果。研究发现:传统数量型货币政策,同时影响经济总需求和总供给,但以需求侧效应为主;针对家庭的定向数量型货币政策,仅影响经济总需求,可兼顾“稳增长”与“稳通缩”,能有效缓解需求侧冲击;针对厂商的定向数量型货币政策,仅影响经济总供给,能同时“稳增长”与“稳通胀”,可有效缓解供给侧冲击。在CBDC时代,货币政策具有“定量+定向”双重属性,将促进货币政策从“大水漫灌”模式进入“精准滴灌”模式。因此,在CBDC功能创新方面,应同时关注交易支付功能和政策实施功能,做到多能并举和智能集成;货币政策创新要为宏观调控立良策谋善治,既要跟上货币形态和功能的演进,又要适应数字经济的发展要求。