With the rapid advancement of cloud computing and the exponential growth of data, the demand for secure data querying and sharing has become increasingly prominent. Identity-Based Encryption with Keyword Search (IBEKS) and Hierarchical IBEKS (HIBEKS) address the issue of secure data querying, as it enables resource-constrained clients to effectively search for encrypted data stored in the cloud. However, existing HIBEKS schemes lack flexible data-query mechanisms among users in the same level, are highly vulnerable to attacks launched by quantum computers and Keyword Guessing Attacks (KGA), and lead to relatively high end-to-end latency. To meet more complex and diverse application requirements and address these vulnerabilities, we introduce a novel primitive called Hierarchical Identity-Based Puncturable Encryption with Keyword Search (HIBPEKS). The decryption keys held by higher-level users are capable of generating decryption keys for lower-level users, thereby enhancing the ability to perform multi-level encrypted data queries within the user group. In addition, to control the query of encrypted data, higher-level users can use specific tags to puncture decryption keys (for lower-level users), so that lower-level users will no longer be able to query the parts of the data associated with the punctured tags. Technically, we have improved the previous lattice-based IBEKS schemes and implemented an efficient and flexible data query mechanism in a hierarchical setting by exploiting Puncturable Encryption (PE) techniques. Moreover, we formalize the security model of HIBPEKS and prove its security within the framework of the random oracle model. Finally, we experimentally evaluate HIBPEKS and show that HIBPEKS is computationally efficient and practical.

In digital currency systems, a users private key determines the ownership of the currency. With the increasing popularity of central bank digital currency (CBDC), secure protections for users private signing keys become more urgent. Compared with transactions between individuals, transactions between corporations have lower frequency but larger amounts, demanding even higher security of the private key. In recent years, scholars have proposed a series of hardware-based private key protection schemes. However, numerous successful attacks on secure hardware have demonstrated that merely relying on hardware protection is insufficient. Program obfuscation is an effective approach for protecting private keys. Therefore, this paper proposes an obfuscatable encrypted SM2 signature scheme and its obfuscator, aiming at enhancing the security of private signing keys in Chinas CBDC transactions between corporations. The correctness and security of the proposed scheme are formally proven, and a series of experimental evaluations are conducted on different mainstream devices. The results show that the proposed scheme improves the security of the users private signing key with relatively low computational overhead, thus obtaining high availability in Chinas CBDC systems.

With the development of the industrial Internet, industrial Internet data has been growing rapidly, and so has the need for secure communications. In the context of industrial communication, it is essential to establish an effective session key between untrusted nodes. The prevailing key management schemes concentrate on key negotiation with the assistance of a central node through a man-in-the-middle approach. However, industrial field environments are typically characterised by harsh conditions, and any node may be damaged or subject to malicious compromise. This can result in the complete paralysis of communication within the entire system. Consequently, existing key management schemes are unable to fulfil the requisite performance requirements. In contrast to previous centralized or polycentric schemes, we propose an asymmetric bivariate polynomials-based novel efficient decentralized key management scheme (ABP-DKM). ABP-DKM achieves threshold switching through a twice-distribution method, which is more secure than other existing schemes. During the whole key negotiation process, ABP-DKM is decentralized. ABP-DKM is capable of not only peer-to-peer communication but also intra-group communication with forward and backward secrecy. The proposed scheme is more secure and efficient than the existing schemes.

In the convergence of the Internet of Things (loT) and blockchain, edge computing enables resource-constrained end devices to offload compute-intensive mining tasks to edge servers to enhance their performance or profits. This calls for a task offloading strategy that accounts for the inherent complexity and variability of the environment, while effectively solving a typically NP-hard offloading problem. Traditional one-shot optimization methods often struggle to adapt to dynamic conditions. Meanwhile, existing learning-based approaches usually rely on centralized frameworks or independent agents, which are inadequate for distributed IoT networks. To this end, a cooperative task offloading strategy is proposed for a blockchain-enabled IoT network with multiple edge service providers. Specifically, the offloading problem is first incorporated into a Markov decision process that considers time-varying channel conditions. A multi-agent deep reinforcement learning algorithm with a gradient estimator is then utilized to optimize the long-term mining utility. Following a centralized training and decentralized execution paradigm, this algorithm allows IoT devices to learn collaborative policies during training while making autonomous decisions based on local observations during execution. Experimental results show that the proposed strategy outperforms existing benchmarks in mining utility under varying conditions.

Trigger-Action Programming (TAP) enables the automation of Internet of Things (IoT) processes by creating rules that trigger specific actions based on certain conditions. As the number of IoT services, including those of multimedia devices and applications, continues to grow, manually selecting the right service pairs for automation becomes increasingly difficult and time-consuming. To address this, we propose CAMCKG, an innovative three-layer framework designed to recommend appropriate action services based on user-defined trigger services. Unlike existing methods, this framework effectively utilizes the complex information and relationships within TAP services to enable more intelligent automation. It introduces a novel combination of attention mechanisms and continuous kernel graph convolution to achieve better information aggregation and personalized recommendations. Experimental results demonstrate that our framework outperforms baseline models, validating its effectiveness and signifying a major advancement in service recommendation for TAP systems.

Ring Confidential Transaction (RingCT) protocols are widely used in cryptocurrencies to protect user privacy. Consequently, a corresponding digital signature scheme, such as a ring signature scheme that hides the signers’ identities, is required. Accordingly, the security of a RingCT protocol depends on the confidentiality of the secret signing keys of the underlying ring signature scheme. However, existing solutions like hardware wallets, Trusted Execution Environments (TEEs), and threshold signature schemes have limitations such as specified expensive hardware, targeting attacks at CPUs on insufficiently secure hardware, and overheads caused by multiple parties. On the contrary, program obfuscation for signature schemes offers advantages over these existing approaches. Concretely, we propose a novel obfuscator that secures the secret keys of the concise linkable spontaneous anonymous group (CLSAG) signature scheme, which is the latest ring signature scheme used in Monero’s RingCT protocol. To achieve enhanced security, the proposed obfuscator leverages Paillier homomorphic encryption to transform secret keys into an obfuscated form resistant to attacks. The security of the proposed obfuscator has been formally proved. Computational efficiency has been both theoretically analyzed and experimentally evaluated with positive results on various testing platforms.

With the rapid growth of wireless sensor networks, secure data transmission, storage, and distribution in such networks has become an urgent demand. To defend against security risks such as data leakage, key compromise, and unauthorized misuse of data simultaneously, we propose a novel obfuscatable ciphertext-policy attribute-based re-encryption scheme with a specially designed obfuscator. The proposed scheme leverages program obfuscation to transform the re-encryption program codes into an unintelligible form and embed the private keys into the obfuscated implementation. Consequently, the proposed scheme protects data confidentiality and keeps the secrecy of the private key while providing fine-grained access control. Formal proofs for the security of the proposed re-encryption scheme and the obfuscator are provided. Extensive experiments have been conducted on representative platforms, including cloud servers, workstations, and embedded devices, to evaluate the computational efficiency and energy consumption of the scheme. Experimental results indicate that the scheme achieves high efficiency on various platforms and economical energy consumption on typical embedded devices with constrained resources.

This study addresses the current lack of research on the effectiveness assessment of Artificial Intelligence (AI) technology in architectural education. Our aim is to evaluate the impact of AI-assisted architectural teaching on student learning. To achieve this, we developed an AI-embedded teaching model. A total of 24 students from different countries participated in this 9-week course, completing a comprehensive analysis of architectural programming and design using AI technologies. This study conducted questionnaire surveys with students at both midterm and final stages of the course, followed by structured interviews after the course completion, to explore the effectiveness and application status of the teaching model. The results indicate that the AI-embedded teaching model positively and effectively influenced student learning. The “innovative capability” and “work efficiency” of AI technologies were identified as key factors affecting the effectiveness of the teaching model. Furthermore, the study revealed a close integration of AI technologies with architectural programming but identified challenges in the uncontrollable expression of architectural design outcomes. Student utilization of AI technologies appeared fragmented, lacking a systematic approach. Lastly, the study provides targeted optimization suggestions based on the current application status of AI technologies among students. This research offers theoretical and practical support for the further integration of AI technologies in architectural education.

Carbon fiber-reinforced polymer (CFRP) composites are extensively used in various engineering applications due to their superior strength-to-weight ratio and excellent mechanical properties. Predicting crack propagation paths in CFRP composites is a complex challenge due to their multiphase nature and intricate microstructural interactions. While finite element (FE) simulations possess significant capabilities for this purpose, they entail substantial computational demands and extended execution times, thereby limiting their viability in applications with high computational requirements. To address this challenge, we propose an end-to-end deep learning framework specifically for predicting crack propagation paths in two-dimensional CFRP composites. Drawing inspiration from semantic segmentation techniques, we employ EfficientNet for feature extraction, enabling the capture of hierarchical and multiscale features from both microstructure images and stress field distributions. A key aspect of our framework is the utilization of multimodal data fusion and self-attention mechanisms to effectively integrate these diverse data sources. The results demonstrate the effectiveness of our multimodal feature integration approach, producing accurate segmentations of crack path. This novel framework offers a promising approach to understanding and predicting failure mechanisms in composite materials, with significant implications for the design and maintenance of advanced composite structures.

Image-text retrieval is a fundamental and crucial task in the field of multimodal interaction, which assists internet users in retrieving the required visual and textual information conveniently. The dominant method for image-text retrieval aims to learn a visual semantic embedding space such that related visual and textual data are close to each other. Recent research focuses on designing sophisticated pooling strategies to better aggregate visual and textual features into holistic embeddings. However, existing methods often use the same pooling operator for the whole dataset, ignoring that samples with diverse intra-modality relationships require pooling operators trained with different parameters. To tackle this issue, we propose a novel Mixture of Pooling Experts (MoPE) framework, which combines multiple pooling operators to aggregate features for different data subsets. Specifically, we introduce a novel route gating strategy in combination with an aggregation expert module to dynamically learn diverse pooling experts for samples in different data subsets. Moreover, to fully exploit the intra-modality relationships, we develop a specialized router with a self-attention gate mechanism to direct each sample to the proper pooling expert. Extensive experiments conducted on two widely used benchmark datasets, namely Flickr30K and MS-COCO, demonstrate the superiority of our method over several state-of-the-art methods.

Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems (CPESs), but such systems have also become popular targets of cyberattacks. Currently, available methods for evaluating the impacts of cyberattacks suffer from limited resilience, efficacy, and practical value. To mitigate their potentially disastrous consequences, this study suggests a two-stage, discrepancy-based optimization approach that considers both preparatory actions and response measures, integrating concepts from social computing. The proposed Kullback-Leibler divergence-based, distributionally robust optimization (KDR) method has a hierarchical, two-stage objective function that incorporates the operating costs of both system infrastructures (e.g., energy resources, reserve capacity) and real-time response measures (e.g., load shedding, demand-side management, electric vehicle charging station management). By incorporating social computing principles, the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks. The preparatory stage entails day-ahead operational decisions, leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks. The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side, taking into account the social context and preferences of energy consumers, to ensure resilient, economically efficient CPES operations. Our method can determine optimal schemes in both stages, accounting for the social dimensions of the problem. An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR, incorporating social computing techniques to enhance the understanding and response to cyber threats. This approach can mitigate the impacts more effectively than several existing methods, even with limited data availability. To extend this risk-neutral model, we incorporate conditional value at risk as an essential risk measure, capturing the uncertainty and diverse impact scenarios arising from social computing factors. The empirical results affirm that the KDR method, which is enriched with social computing considerations, produces resilient, economically efficient solutions for managing the impacts of cyberattacks on a CPES. By integrating social computing principles into the optimization framework, it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs, ultimately improving the overall resilience and effectiveness of the system's response measures.

User transaction data are rich, valuable, but sensitive. With the huge amounts of transaction data, data mining algorithms can make many applications practical, such as customer-behavior analysis, marketing, and forensics. The value behind the transaction data analysis on the other hand raises the risk of data leak. In this paper, we introduce a Crypto-based KMeans clustering algorithm (CTKM) on the Transaction data of web users for user clustering and data protection as well. Considering the categoricalness of user transaction data, a taxonomy-based distance has been employed, which is applicable to the data encryption process also. In order to obtain efficient computations on the distance, a distance batch computing(DBC) protocol is designed and deployed in a two-server platform. We theoretically estimate both the computation and communication costs of the algorithm. Experimental results on a real data set demonstrate its practical value on web user clustering.

目的 设计基于疾病诊断相关分组(DRG)病种分类的医院科室运营方案,优化病种结构,提高运行效率,有效控制费用.方法 制定基于DRG病种分类的科室运营方案的实施路径,应用比较分析法和非参数检验法对方案实施前后的科室运行数据进行分析.结果 科室运行中反映病种结构、疾病疑难危重程度、运行效率和费用控制的指标在方案实施前后比较均有统计学意义(P＜0.05).结论 基于DRG病种分类的科室运营方案实施有助于优化科室病种结构,增强疑难危重疾病诊疗能力,提高科室资源配置与运营效率,有效降低患者就医费用.

Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM Datasets and made them publicly available for research purposes. This paper describes the datasets, which was acquired on Type 1 (n = 12) and Type 2 (n = 100) diabetic patients in Shanghai, China. The acquisition has been made in real-life conditions. The datasets contain the clinical characteristics, laboratory measurements and medications of the patients. Moreover, the continuous glucose monitoring readings with 3 to 14 days as a period together with the daily dietary information are also provided. The datasets can contribute to the development of data-driven algorithms/models and diabetes monitoring/managing technologies.

Scientific performance-based salary system is of great significance for hospitals to implement effective internal management, improve the enthusiasm of medical staff, and promote the high-quality development of public hospitals. Based on the requirements of the national salary system reform, a tertiary general hospital has been designing and implementing a performance-based salary system since 2016. The hospital has established a job evaluation index system to stratify various job sequences in the hospital, and determined the job value coefficients for different sequences and levels. It has also established a performance salary project system that covered job performance salary, specific performance salary, and innovative performance salary. In addition, the hospital has established a performance-based salary management system that covered salary standard formulation, performance salary accounting and distribution, and dynamic adjustment of the performance-based salary management system. The application of this performance-based compensation management system has achieved the matching of employee value, job hierarchy, and medical services. From 2016 to 2020, employees′ overall satisfaction with performance-based salary exceeded 85%. At the same time, the system could enhance the operational efficiency and quality of the hospital, drive technological development and scientific research innovation, playing a positive incentive role in the high-quality development of the hospital.

Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.

目的 构建顺应按疾病诊断相关分组(DRG)支付方式改革的、符合公立医院管理需要的公立医院运营效率评价指标体系.方法 通过分析DRG支付方式下公立医院运营管理的变革搭建了评价模型,采用德尔菲法遴选DRG支付方式下公立医院运营效率评价指标,利用结构方程模型确定指标体系权重.结果 建立了包含4个一级指标和17个二级指标的DRG支付方式下公立医院运营效率评价指标体系.4个一级指标为资源效率(0.251 9)、服务效率(0.249 4)、财务效率(0.251 9)、管理效率(0.246 8).结论 构建基于DRG的公立医院运营效率评价指标体系,可引导医院在DRG支付方式改革环境下,加强运营管理,实现创新转型,助推高质量发展.

Learning web user embedding based on interaction data in the context of taxonomy is a way of studying the correlation between two web users. Such user embedding is important for further user analysis. Interaction data is made up of users and the items they interact within a domain, which is a group of entities with a basic common property. Usually a taxonomy of these items that users interact with is a hierarchical category structure for a domain. However, the taxonomy is not totally suitable for a particular task. To solve this problem, we propose a dual-way method DualTaxoVec, which learns the user embedding based on the taxonomy of the user interaction items. Meanwhile, it automatically constructs the taxonomy for the items that adapts the domain of users. It is composed of user–item and item–user tracks to construct the taxonomy and embed users in a dual-way. According to the experimental results, the validity and effectiveness of the DualTaxoVec has been demonstrated.

Trigger-Action Programming (TAP) is a popular IoT programming paradigm that enables users to connect IoT services and automate IoT workflows by creating if-trigger-then-action rules. However, with the increasing number of IoT services, specifying trigger and action services to compose TAP rules becomes progressively challenging for users due to the vast search space. To facilitate users in programming, a novel method named TAP-AHGNN is proposed to recommend feasible action services to auto-complete the rule based on the user-specified trigger service. Firstly, a heterogeneous TAP knowledge graph is designed, from which five meta-paths can be extracted to construct services’ neighborhoods. Then, the model incorporates a multi-level attention-based heterogeneous graph convolution module that selectively aggregates neighbor information, and a transformer-based fusion module that enables the integration of multiple types of features. With the two modules mentioned before, the final representations of services can capture both semantic and structural information, which helps generate better recommendation results. Experiments on the real-world dataset demonstrate that TAP-AHGNN outperforms the most advanced baselines at HR@k, NDCG@k and MRR@k. To the best of our knowledge, TAP-AHGNN is the first method for service recommendation on TAP platforms using the heterogeneous graph neural network technique.

按疾病诊断相关分组(DRG)支付方式改革的目标是实现"医院、医保、患者"共赢,体现的是医院、医保、患者价值的一致性,将驱动公立医院在运营管理中更加关注质量、效率、安全、服务、成本等要素.DRG支付方式改革将对公立医院运营管理带来深远的影响,公立医院必须将自身发展与DRG融合在一起,结合运营管理现状,充分利用DRG支付方式改革带来的机遇创新转型,积极调整运营战略,探索运营管理路径,构建有效的运营管理体系,推动公立医院高质量发展.