With the rapid development of informatization, a vast amount of data is continuously generated and accumulated, leading to the emergence of cloud storage services. However, data stored in the cloud is beyond the control of users, posing various security risks. Cloud data auditing technology enables the inspection of data integrity in the cloud without the necessity of data downloading. Among these, public auditing schemes have experienced rapid development due to their ability to avoid additional user auditing expenses. However, malicious third-party auditors can compromise data privacy. This paper proposes an improved identity-based cloud auditing scheme that can resist malicious auditors. This scheme is also constructed on an identity-based public auditing scheme using blockchain to prevent malicious auditing. We found the scheme is not secure because a malicious cloud server can forge authentication tags for outsourced data blocks, while our scheme has not these security flaws. Through security proofs and performance analysis, we further demonstrate that our scheme is secure and efficient. Additionally, our scheme has typical application scenarios.

The Internet of Vehicles (IoV) counts for much in advancing intelligent transportation by connecting people, vehicles, infrastructures, and cloud servers (CS). However, the open-access wireless channels within the IoV are susceptible to malicious attacks. Therefore, an authentication key agreement protocol becomes essential to ensure secure vehicular communications and protect vehicle privacy. Nevertheless, although the vehicles in the group are compromised, they can still update the group key and obtain the communication content in the existing group key agreement protocols. Therefore, it is still challenging to guarantee post-compromise forward security (PCFS). Dynamic key rotation is a common approach to realizing PCFS, which brings a heavy computation and communication burden. To address these issues, an efficient and robust continuous group key agreement (ER-CGKA) scheme with PCFS is designed for IoV. The propose-and-commit flow is employed to support asynchronous group key updates. Besides, the computation cost and communication overhead are significantly reduced based on the TreeKEM architecture. Furthermore, we adopt the threshold mechanism to resist the collusion attacks of malicious vehicles, which enhances the ER-CGKA scheme\'s robustness. Security analysis indicates that the proposed scheme satisfies all the fundamental security requirements of the IoV and achieves PCFS. The performance evaluation results show that our ER-CGKA scheme demonstrates a reduction in the computation cost of 18.82% (Client) and 33.18% (CS) approximately, and an increase in communication overhead of around 55.57% since pseudonyms are utilized to achieve conditional privacy-preserving. Therefore, our ER-CGKA scheme is secure and practical.

Recent years, edge-cloud computing has attracted more and more attention due to benefits from the combination of edge and cloud computing. Task scheduling is still one of the major challenges for improving service quality and resource efficiency of edge-clouds. Though several researches have studied on the scheduling problem, there remains issues needed to be addressed for their applications, e.g., ignoring resource heterogeneity, focusing on only one kind of requests. Therefore, in this paper, we aim at providing a heterogeneity aware task scheduling algorithm to improve task completion rate and resource utilization for edge-clouds with deadline constraints. Due to NP-hardness of the scheduling problem, we exploit genetic algorithm (GA), one of the most representative and widely used meta-heuristic algorithms, to solve the problem considering task completion rate and resource utilization as major and minor optimization objectives, respectively. In our GA-based scheduling algorithm, a gene indicates which resource that its corresponding task is processed by. To improve the performance of GA, we propose to exploit a skew mutation operator where genes are associated to resource heterogeneity during the population evolution. We conduct extensive experiments to evaluate the performance of our algorithm, and results verify the performance superiority of our algorithm in task completion rate, compared with other thirteen classical and up-to-date scheduling algorithms.

The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers\' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

The cross-disciplinary virtually simulated platform for enterprise management in universities\' new business courses is an initiative practical framework based on scenario-driven tasks. However, there is a prominent conflict between the rapid operating cycle of simulation enterprises plus their fierce competitions and the strategic demand for real-time analysis for operational data. Based on such demand, this study takes the development method of the simulated enterprise management cockpit from Guangzhou Huashang College as an example. It adopts the combined weighting method based on cloud models to determine indicator weights, then qualitative and quantitative data analyses are conducted from five aspects: \"business, finance and operation\", \"customer management and marketing\", \"internal operational objectives\", \"product development strategy\", along with \"team building and management\". This approach achieves a comprehensive evaluation and early warning of the enterprise management process. Specifically, the subjective weights are determined by the Analytic Hierarchy Process, while the objective weights by the entropy weight method, finally verified by cloud model evaluation of its overall indicator performance. The design can evaluate the comprehensive performance of enterprise management indicators and students\' activity participation through the cloud-based application and the digital cockpit, so as to fully presents the enterprise\'s overall management level, along with judgement of whether it is reasonable through pointers in different colors. In addition, apparent indicator-related characteristics are also utilized to assess future decision-making directions. Finally, this comprehensive approach can timely optimize operation strategies and facilitate budget allocation for future development.

Objective: To explore the feasibility and safety of remote programming technology based on 5G cloud technology support platform in postoperative follow-up of cardiovascular implantable electronic devices(CIED). Methods: This study was a multicenter cross-sectional study. CIED patients from 12 hospitals lacking full-time follow-up specialists in Sichuan Province were enrolled from June 2021 to October 2021. All patients\' devices received remote inspecting and programming by the follow-up specialist of the remote follow-up center of the Third People\'s Hospital of Chengdu through 5G cloud technology support platform. The baseline data, device alarm events, device reprogramming events, adverse reactions and satisfaction questionnaire survey results were collected. Results: A total of 195 CIED implantation patients were included, with an age of (72.5±11.3) years, including 103 males (52.6%). All patients completed remote inspecting and programming successfully, with a duration of (5.8±4.0) min. Ninety-one patients\' CIED were reprogrammed, with a total of 104 parameter adjustments. No abnormal communication or adverse events occurred. The satisfaction questionnaire showed that 97.9%(191/195) of the patients trusted or relatively trusted remote follow-up and 86.7%(169/195) of the patients were willing to choose remote follow-up mode for device management. Conclusion: The remote programming based on 5G cloud technology support platform may be feasible and safe for postoperative follow-up of CIED patients.
目的： 探索基于5G云技术支持平台的远程程控技术在心脏置入型电子器械（CIED）术后随访中的可行性和安全性。 方法： 本研究为多中心横断面研究。选取2021年6至10月在四川省12家暂缺随访专员的综合医院的CIED置入患者，由成都市第三人民医院远程随访中心的随访专员通过5G云技术支持平台对患者器械进行实时远程检查和远程程控。收集患者的基线资料、器械报警事件、再程控事件、不良反应及满意度问卷调查结果。 结果： 共纳入195例CIED置入患者，年龄（72.5±11.3）岁，男性103例（52.6%），所有患者均成功完成远程检查和程控，用时（5.8±4.0）min。根据随访结果，91例患者的CIED需要再程控，累计参数调整104次。所有程控过程通信情况无异常，患者未发生任何不良事件。满意度问卷调查显示，97.9%（191/195）的患者信任或比较信任远程随访，86.7%（169/195）的患者愿意选择远程随访模式进行设备管理。 结论： 基于5G云技术支持平台的远程程控技术在CIED患者术后随访中的临床应用可能是可行、安全的。.

With the cutting-edge advancements in computer vision, facial expression recognition (FER) is an active research area due to its broad practical applications. It has been utilized in various fields, including education, advertising and marketing, entertainment and gaming, health, and transportation. The facial expression recognition-based systems are rapidly evolving due to new challenges, and significant research studies have been conducted on both basic and compound facial expressions of emotions; however, measuring emotions is challenging. Fueled by the recent advancements and challenges to the FER systems, in this article, we have discussed the basics of FER and architectural elements, FER applications and use-cases, FER-based global leading companies, interconnection between FER, Internet of Things (IoT) and Cloud computing, summarize open challenges in-depth to FER technologies, and future directions through utilizing Preferred Reporting Items for Systematic reviews and Meta Analyses Method (PRISMA). In the end, the conclusion and future thoughts are discussed. By overcoming the identified challenges and future directions in this research study, researchers will revolutionize the discipline of facial expression recognition in the future.

Outsourcing data to remote cloud providers is becoming increasingly popular amongst organizations and individuals. A semi-trusted server uses Searchable Symmetric Encryption (SSE) to keep the search information under acceptable leakage levels whilst searching an encrypted database. A dynamic SSE (DSSE) scheme enables the adding and removing of documents by performing update queries, where some information is leaked to the server each time a record is added or removed. The complexity of structures and cryptographic primitives in most existing DSSE schemes makes them inefficient, in terms of storage, and query requests generate overhead costs on the Smart Device Client (SDC) side. Achieving constant storage cost for SDCs enhances the viability, efficiency, and easy user experience of smart devices, promoting their widespread adoption in various applications while upholding robust privacy and security standards. DSSE schemes must address two important privacy requirements: forward and backward privacy. Due to the increasing number of keywords, the cost of storage on the client side is also increasing at a linear rate. This article introduces an innovative, secure, and lightweight Dynamic Searchable Symmetric Encryption (DSSE) scheme, ensuring Type-II backward and forward privacy without incurring ongoing storage costs and high-cost query generation for the SDC. The proposed scheme, based on an inverted index structure, merges the hash table with linked nodes, linking encrypted keywords in all hash tables. Achieving a one-time O(1) storage cost without keyword counters on the SDC side, the scheme enhances security by generating a fresh key for each update. Experimental results show low-cost query generation on the SDC side (6,460 nanoseconds), making it compatible with resource-limited devices. The scheme outperforms existing ones, reducing server-side search costs significantly.