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In the context of the digital evolution of the logistics industry, the interconnection of logistics information systems and associated data have become an obstacle of business collaboration among various stakeholders. A critical challenge in this domain is ensuring controllable access to logistics business data, given the industry’s current state characterized by independence, autonomy, disconnection, and heterogeneity, alongside the non-negotiable requirement for data privacy. We propose a novel model of trusted data access control based on of logistics business collaboration semantics. This approach incorporates semantic inference technologies into attribute-based access control mechanisms, thereby enabling the streamlined formulation of access control policies and facilitating unified authorization and control. Moreover, the method addresses the issue of access control policy management and maintenance at the semantic level. The proposed solution can pave the way for enhanced business collaboration between business entities, and further enable the building of a data exchange service platform within the logistics industry.

If the product supply shortage during the sales period, customers will turn to other company, so the enterprise will shortage the sales opportunity. The enterprise can predict product demands, and manages the product sales. To use the supply chain management prediction system to improve the bottleneck of the inventory; thus, the hot sales product has more critical time utilization and the inventory status reflected quickly in Internet of Things (IoT). Because replenishment model cannot show the actual quantity of products on the store shelves. Therefore, this article proposed the intelligent agent-based predict system, that agent-based serves as framework to construct an integrated predict system, use radio frequency identification (RFID) technology to design the intelligent product predict shelf to extract products message, and the service oriented architecture to develop an predict information to provide the product as recommend customer services. The result of the paper, to propose an agent-based cloud computing service platform in IoT, and intelligent agents with SOA as backend cloud services. To build a prototype predict system with performance analysis. The results proved that the predict system architecture for intelligent agent-based predict system could increase operation performance and enhance effectively for customer service quality for hot sale products.

The extensive application of the Internet of Things (IoT) and artificial intelligence technology has greatly promoted the construction and development of smart cities. Smart home as the foundation of smart cities can optimize home lifestyles. However, users access the smart home system through public channels, and the transmitted information is vulnerable to attack by attackers, and the privacy and data security of the home user will be difficult to be guaranteed. Therefore, how to protect users’ data and privacy security becomes critical. In this paper, we design a provably secure authentication scheme for the smart home environment, which ensures that only legitimate users can use smart devices. We use the informal model to verify the security of the scheme and formally analyze the security and correctness of the scheme through the Real or Random model. Finally, through the comparison of security and performance analysis, it is proven that our scheme has higher security under similar performance.

In recent years, blockchain has received increasing attention and numerous applications have emerged from this technology. A renowned Blockchain application is the cryptocurrency Bitcoin, that has not only been effectively solving the double-spending problem but also it can confirm the legitimacy of transactional records without relying on a centralized system to do so. Therefore, any application using Blockchain technology as the base architecture ensures that the contents of its data are tamper-proof. This paper uses the decentralized Blockchain technology approach to ensure that consumers do not fully rely on the merchants to determine if products are genuine. We describe a decentralized Blockchain system with products anti-counterfeiting, in that way manufacturers can use this system to provide genuine products without having to manage direct-operated stores, which can significantly reduce the cost of product quality assurance.

The smart home is a crucial embodiment of the internet of things (IoT), which can facilitate users to access smart home services anytime and anywhere. Due to the limited resources of cloud computing, it cannot meet users’ real-time needs. Therefore, edge computing emerges as the times require, providing users with better real-time access and storage. The application of edge computing in the smart home environment can enable users to enjoy smart home services. However, users and smart devices communicate through public channels, and malicious attackers may intercept information transmitted through public channels, resulting in user privacy disclosure. Therefore, it is a critical issue to protect the secure communication between users and smart devices in the smart home environment. Furthermore, authentication protocols in smart home environments also have some security challenges. In this paper, we propose an anonymous authentication protocol that applies edge computing to the smart home environment to protect communication security between entities. To protect the security of smart devices, we embed physical unclonable functions (PUF) into each smart device. Real-or-random model, informal security analysis, and ProVerif are adopted to verify the security of our protocol. Finally, we compare our protocol with existing protocols regarding security and performance. The comparison results demonstrate that our protocol has higher security and slightly better performance.

Global routing is an important link in very large scale integration (VLSI) design. As the best model of global routing, X-architecture Steiner minimal tree (XSMT) has a good performance in wire length optimization. XSMT belongs to non-Manhattan structural model, and its construction process cannot be completed in polynomial time, so the generation of XSMT is an NP hard problem. In this paper, an X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (XSMT-MoDDE) is proposed. Firstly, an effective encoding strategy, a fitness function of XSMT, and an initialization strategy of population are proposed to record the structure of XSMT, evaluate the cost of XSMT and obtain better initial particles, respectively. Secondly, elite selection and cloning strategy, multiple mutation strategies, and adaptive learning factor strategy are presented to improve the search process of discrete differential evolution algorithm. Thirdly, an effective refining strategy is proposed to further improve the quality of the final Steiner tree. Finally, the results of the comparative experiments prove that XSMT-MoDDE can get the shortest wire length so far, and achieve a better optimization degree in the larger-scale problem.

The interaction between children and books is an essential part of the reading experience. Publishers all over the world are working to cultivate reading habits in children and attract attention to traditional books. Considering the invaluable nature of these early reading experiences. This paper investigated the effects of book interaction design on 5–6 years old children, taking into account reading preferences, measuring reading time, and emotional response to improve their reading experience and potentially design books according to these interactions. The results showed that preschool children (5–6 years old) prefer sensory interaction, and that book interaction design has a significant influence on reading time, affective experience, and subjective ratings. Girls around 5–6 years preferred folding interaction and pop-up interaction in reading. This study summarizes these results in order to provide practical guidelines for book publishers, enabling them to design better books for children.

In most antivirus products, the local cache mechanism is implemented for the purpose of reducing the time of the file scan. However, the database designed to store cache is limited, a lot of times, that most of the cache data have been purged during the file scanning since it cannot store too many CRC files in the local cache. Traditional methods of building and forming cache cannot effectively solve the problem of low cache utilization. Whereas a client with a traditional cache mechanism needs to send out multiple queries in order to accumulate a usable cache one-by-one, which causes the waste of database and time. In this paper, we first gather the current feedback data and analyze the data, and then, we explore an effective Two-Step clustering scheme to implement for the optimization of the prefetch cache strategy. Our scheme can avoid the cache being cleared prematurely and increase the probability of cache hits. Extensive experimental results demonstrate that our proposed scheme could save 11.76% time during file scanning.

The construction of the financial module is very important in the intelligent management of the company. The collection, transmission, and processing of relevant item information in the financial module are of great significance to the construction of the intelligent financial module. The intelligent financial processing module can improve financial office efficiency and increase the security of the financial processing process. Section 404 of the Sarbanes-Oxley Act (SOX 404) has always played a decisive role in the quality and transparency of the financial modules of the Enterprise Resource Planning (ERP) of multinational corporations, due to the mandatory nature of the Securities and Exchange Commission (SEC). The purpose of this research is to design a set of intelligent ERP financial modules for multinational corporations that could comply with SOX 404, strengthen internal controls, and improve the security and convenience of the financial module. Thereby achieving intelligent management of financial modules and intelligent prevention of risks. Our solution could fortify the preventive controls over inappropriate payments to products with quality issues, reduce manual efforts for credit memo issuance and receipt. It could also fortify the preventive controls over financial risks potentially threatened by a sudden contingent liability concerning E&O issues. It could prevent potential and unexpected frauds arisen by a malicious user to meet the requirements of SOX over preventive and detective controls over the financial system. It could also ensure the consistency of journals between the preparer’s entry and review’s approval as well as improve the completeness and timeliness of financial statement assertions.

A good set of the financial system could take care of all accounting entries and their impacts on the whole intelligent financial environment. The total flow of money and total expenditures will be reflected here. The intelligent financial system can improve the utilization of data, increase the work efficiency of financial personnel, and increase the security of financial processing services. This system could help the managers make their important financial decisions, financial budgeting, and so on. They can know about the financial conditions at any time. Based on the collection and processing of financial data, we design and analyze the related financial system to carry out the intelligent property of the financial environments. The system extends the quality of financial reporting, SOX compliance, and internal controls in a financial system, including credit management, revenue recognition, bad debts allowance. Once there is an early warning in the financial system, our proposed enhanced system could give a fast early response, and bridge a positive linkage about the planning department, finance department, and top managers, which could avoid potential exposure to bad debts. This system guarantees that the credible customers are selected, and avoid the potential risk of bad debts are incurred by a loose credit limit. It strengthens the preventive control over aging receivables management and detects the potential risk of bad debts in the earlier system alert.

The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, Due to storage space limitations, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilizes data mining technique with clustering concept, by gathering the current feedback data that have from our SPN (Smart Protection Network). we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototype, this design can really reduce network traffic more than 8%.

Gaussian process regression (GPR) is frequently used for uncertain measurement and prediction of nonstationary time series in the Internet of Things data, nevertheless, the generalization and regression efficacy of GPR are directly impacted by its selection of hyper-parameters. In the study, a non-inertial particle swarm optimization with elite mutation-Gaussian process regression (NIPSO-GPR) is proposed to optimize the hyper-parameters of GRP. NIPSO-GPR can adaptively obtain hyper-parameters of GPR via uniform non-inertial velocity update formula and adaptive elite mutation strategy. When compared with several frequently used algorithms of hyper-parameters optimization on linear and nonlinear time series sample data, experimental results indicate that GPR after hyper-parameters optimized by NIPSO-GPR has better fitting precision and generalization ability.

The wireless sensor networks is a new technology for information acquisition and processing, which includes the techniques of sensor, computer, Internet of Things and network. Due to the fragility of wireless sensor networks, the security issue has become a prevalent concern in the wireless communication for Internet of Things. This paper offers a deep insight to the secrecy capacity of wireless sensor network and a calculable threshold of capacity based on the amplify-and-forward (AF) compressed sensing scheme. Moreover, we provide a feasible algorithm based on augmented Lagrange method for source reconstruction for the legitimate nodes and un-authorized nodes. Furthermore, we also discuss the impact of the numbers of active sensor nodes, relay nodes and eavesdropper nodes to the secrecy capacity. Simulation results demonstrate the correctness of the derived secrecy capacity.