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With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%. [ABSTRACT FROM AUTHOR]

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study's potential to impact accounting systems and financial reporting research and practice emphasizes its importance. [ABSTRACT FROM AUTHOR]

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations' data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in "digital sustainability" and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology. [ABSTRACT FROM AUTHOR]

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data's authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model. [ABSTRACT FROM AUTHOR]

With the growing global emphasis on renewable energy, the issue of renewable energy consumption has emerged as a hot topic of current research. In response to the volatility and uncertainty in the process of renewable energy consumption, this study proposes a renewable energy consumption strategy based on scenario reduction and flexible resource utilization. This strategy aims to achieve the efficient utilization of renewable energy sources through optimized resource allocation while ensuring the stable operation of the power system. Firstly, this study employs scenario analysis methods to model the volatility and uncertainty of renewable energy generation. By applying scenario reduction techniques, typical scenarios are selected to reduce the complexity of the problem, providing a foundation for the construction of the optimization model. At the same time, by fully considering the widely available small-capacity energy storage units within the system, a flexible cloud energy storage scheduling model is constructed to assist in renewable energy consumption. Finally, the validity and feasibility of the proposed method are demonstrated through case studies. Through analysis, the proposed scenario generation method achieved a maximum value of 26.28 for the indicator I D B I and a minimum value of 1.59 for the indicator I C H I . Based on this foundation, the cloud energy storage model can fully absorb renewable energy, reducing the net load peak-to-trough difference to 1759 kW, a decrease of 809 kW compared with the traditional model. [ABSTRACT FROM AUTHOR]

Existing attribute-based proxy re-encryption schemes suffer from issues like complex access policies, large ciphertext storage space consumption, and an excessive authority of the authorization center, leading to weak security and controllability of data sharing in cloud storage. This study proposes a Weighted Attribute Authority Multi-Authority Proxy Re-Encryption (WAMA-PRE) scheme that introduces attribute weights to elevate the expression of access policies from binary to multi-valued, simplifying policies and reducing ciphertext storage space. Simultaneously, the multiple attribute authorities and the authorization center construct a joint key, reducing reliance on a single authorization center. The proposed distributed attribute authority network enhances the anti-attack capability of cloud storage. Experimental results show that introducing attribute weights can reduce ciphertext storage space by 50%, proxy re-encryption saves 63% time compared to repeated encryption, and the joint key construction time is only 1% of the benchmark scheme. Security analysis proves that WAMA-PRE achieves CPA security under the decisional q-parallel BDHE assumption in the random oracle model. This study provides an effective solution for secure data sharing in cloud storage. [ABSTRACT FROM AUTHOR]

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or "cold" block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers' costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes. [ABSTRACT FROM AUTHOR]

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems. [ABSTRACT FROM AUTHOR]

In recent years, the security of cloud storage has become a topic attracting significant attention due to a series of features such as large storage space, high availability, and low cost. Although traditional plain text images can withstand external attacks, the usability of images is completely lost. In order to balance the usability and privacy of images, some scholars have proposed the thumbnail-preserving encryption (TPE) scheme. The ideal TPE algorithm can keep the same thumbnail before and after encryption, which reduces the time cost and strengthens the resistance to attacks, but the existing schemes cannot fulfill the above criteria. In this paper, we propose a new TPE scheme that combines bit-transform encryption and improved hierarchical encryption. By constructing a chaotic system, both encryption and decryption times are shortened, while the randomness of the selected cells is enhanced. In addition, the Hamming distance is introduced to classify and scramble the binary encryption units. The experimental results show that when the number of thumbnail chunks is 16 × 16, the encryption and decryption time decreases to 4 s, and the SSIM value after encryption is close to 1, which indicates that the thumbnail before and after satisfying the encryption basically remains the same, and when the number of chunks is gradually increased, the success rate of the face detection tends to be close to 0. In addition, as the number of experimental iterations increases, the encryption effect improves with an increasing ability to resist attacks. [ABSTRACT FROM AUTHOR]

In this work, a language-level Semantics-Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of the point feature distribution, as well as the pseudo-feature generation, and a feature–geometry-based Mixup approach is further proposed to facilitate the distribution learning. Since a large number of point features could be generated from the learned distribution thanks to the semantics-conditioned modeling, any existing segmentation network could be embedded into the proposed framework to boost its performance. In addition, the proposed framework has the inherent advantage of dealing with novel classes, which seems an impossible feat for the current segmentation networks. Extensive experimental results on two public datasets demonstrate that three typical segmentation networks could achieve significant improvements over their original performances after enhancement by the proposed framework in the conventional 3D segmentation task. Two benchmarks are also introduced for a newly introduced zero-shot 3D segmentation task, and the results also validate the proposed framework. [ABSTRACT FROM AUTHOR]

With the surge in cloud storage popularity, more individuals are choosing to store large amounts of data on remote cloud service providers (CSPs) to save local storage resources. However, users' primary worries revolve around maintaining data integrity and authenticity. Consequently, several cloud auditing methods have emerged to address these concerns. Many of these approaches rely on traditional public-key cryptography systems or are grounded in identity-based cryptography systems or certificateless cryptography systems. However, they are vulnerable to the increased costs linked with certificate management, key escrow, or the significant expenses of establishing a secure channel, respectively. To counter these limitations, Li et al. introduced a certificate-based cloud auditing protocol (LZ22), notable for its minimal tag generation overhead. Nonetheless, this protocol exhibits certain security vulnerabilities. In this paper, we devise a counterfeiting technique that allows the CSP to produce a counterfeit data block with an identical tag to the original one. Our counterfeiting method boasts a 100% success rate ∀ data block and operates with exceptional efficiency. The counterfeiting process for a single block of 10 kB, 50 kB, and 100 kB takes a maximum of 0.08 s, 0.51 s, and 1.04 s, respectively. By substituting the exponential component of homomorphic verifiable tags (HVTs) with non-public random elements, we formulate a secure certificate-based cloud auditing protocol. In comparison to the LZ22 protocol, the average tag generation overhead of our proposed protocol is reduced by 6.80%, 13.78%, and 8.66% for data sizes of 10 kB, 50 kB, and 100 kB, respectively. However, the auditing overhead of our proposed protocol shows an increase. The average overhead rises by 3.05%, 0.17%, and 0.45% over the LZ22 protocol's overhead for data sizes of 10 kB, 50 kB, and 100 kB, correspondingly. [ABSTRACT FROM AUTHOR]

Accurately determining the attitude of non-cooperative spacecraft in on-orbit servicing (OOS) has posed a challenge in recent years. In point cloud-based spatial non-cooperative target attitude estimation schemes, high-precision point clouds, which are more robust to noise, can offer more accurate data input for three-dimensional registration. To enhance registration accuracy, we propose a noise filtering method based on moving least squares microplane projection (mpp-MLS). This method retains salient target feature points while eliminating redundant points, thereby enhancing registration accuracy. Higher accuracy in point clouds enables a more precise estimation of spatial target attitudes. For coarse registration, we employed the Random Sampling Consistency (RANSAC) algorithm to enhance accuracy and alleviate the adverse effects of point cloud mismatches. For fine registration, the J-ICP algorithm was utilized to estimate pose transformations and minimize spacecraft cumulative pose estimation errors during movement transformations. Semi-physical experimental results indicate that the proposed attitude parameter measurement method outperformed the classic ICP registration method. It yielded maximum translation and rotation errors of less than 1.57 mm and 0.071°, respectively, and reduced maximum translation and rotation errors by 56% and 65%, respectively, thereby significantly enhancing the attitude estimation accuracy of non-cooperative targets. [ABSTRACT FROM AUTHOR]

Moving high-performance computing (HPC) applications from HPC clusters to cloud computing clusters, also known as the HPC cloud, has recently been proposed by the HPC research community. Migrating these applications from the former environment to the latter can have an important impact on their performance, due to the different technologies used and the suboptimal use and configuration of cloud resources such as heterogeneous storage. Probabilistic models can be applied to predict the performance of these applications and to optimise them for the new system. Modelling the performance in the HPC cloud of applications that use heterogeneous storage is a difficult task, due to the variations in performance. This paper presents a novel model based on Extreme Value Theory (EVT) for the analysis, characterisation and prediction of the performance of HPC applications that use heterogeneous storage technologies in the cloud and high-performance distributed parallel file systems. Unlike standard approaches, our model focuses on extreme values, capturing the true variability and potential bottlenecks in storage performance. Our model is validated using return level analysis to study the performance of representative scientific benchmarks running on heterogeneous cloud storage at a large scale and gives prediction errors of less than 7%. [ABSTRACT FROM AUTHOR]

Deploying high-accuracy detection models on lightweight edge devices (e.g., drones) is challenging due to hardware constraints. To achieve satisfactory detection results, a common solution is to compress and transmit the images to a cloud server where powerful models can be used. However, the image compression process for transmission may lead to a reduction in detection accuracy. In this paper, we propose a feature compression method tailored for object detection tasks, and it can be easily integrated with existing learned image compression models. In the method, the encoding process consists of two steps. Firstly, we use a feature extractor to obtain the low-level feature, and then use a mask generator to obtain an object mask to select regions containing objects. Secondly, we use a neural network encoder to compress the masked feature. As for decoding, a neural network decoder is used to restore the compressed representation into the feature that can be directly inputted into the object detection model. The experimental results demonstrate that our method surpasses existing compression techniques. Specifically, when compared to one of the leading methods—TCM2023—our approach achieves a 25.3% reduction in compressed file size and a 6.9% increase in mAP0.5. [ABSTRACT FROM AUTHOR]

The proliferation of cloud computing has amplified the need for robust privacy-preserving technologies, particularly when dealing with sensitive financial and human resources (HR) data. However, traditional differential privacy methods often struggle to balance rigorous privacy protections with maintaining data utility. This study introduces DPShield, an optimized adaptive framework that enhances the trade-off between privacy guarantees and data utility in cloud environments. DPShield leverages advanced differential privacy techniques, including dynamic noise-injection mechanisms tailored to data sensitivity, cumulative privacy loss tracking, and domain-specific optimizations. Through comprehensive evaluations on synthetic financial and real-world HR datasets, DPShield demonstrated a remarkable 21.7% improvement in aggregate query accuracy over existing differential privacy approaches. Moreover, it maintained machine learning model accuracy within 5% of non-private benchmarks, ensuring high utility for predictive analytics. These achievements signify a major advancement in differential privacy, offering a scalable solution that harmonizes robust privacy assurances with practical data analysis needs. DPShield's domain adaptability and seamless integration with cloud architectures underscore its potential as a versatile privacy-enhancing tool. This work bridges the gap between theoretical privacy guarantees and practical implementation demands, paving the way for more secure, ethical, and insightful data usage in cloud computing environments. [ABSTRACT FROM AUTHOR]

Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment. [ABSTRACT FROM AUTHOR]

Existing point cloud object detection algorithms struggle to effectively capture spatial features across different scales, often resulting in inadequate responses to changes in object size and limited feature extraction capabilities, thereby affecting detection accuracy. To solve this problem, we present a point cloud object detection method based on multi-scale feature fusion of the original point cloud and projection, which aims to improve the multi-scale performance and completeness of feature extraction in point cloud object detection. First, we designed a 3D feature extraction module based on the 3D Swin Transformer. This module pre-processes the point cloud using a 3D Patch Partition approach and employs a self-attention mechanism within a 3D sliding window, along with a downsampling strategy, to effectively extract features at different scales. At the same time, we convert the 3D point cloud to a 2D image using projection technology and extract 2D features using the Swin Transformer. A 2D/3D feature fusion module is then built to integrate 2D and 3D features at the channel level through point-by-point addition and vector concatenation to improve feature completeness. Finally, the integrated feature maps are fed into the detection head to facilitate efficient object detection. Experimental results show that our method has improved the average precision of vehicle detection by 1.01% on the KITTI dataset over three levels of difficulty compared to Voxel-RCNN. In addition, visualization analyses show that our proposed algorithm also exhibits superior performance in object detection. [ABSTRACT FROM AUTHOR]

The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems. [ABSTRACT FROM AUTHOR]

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics. [ABSTRACT FROM AUTHOR]

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively. [ABSTRACT FROM AUTHOR]

The smart grid, as a crucial part of modern energy systems, handles extensive and diverse data, including inputs from various sensors, metering devices, and user interactions. Outsourcing data storage to remote cloud servers presents an economical solution for enhancing data management within the smart grid ecosystem. However, ensuring data privacy before transmitting it to the cloud is a critical consideration. Therefore, it is common practice to encrypt the data before uploading them to the cloud. While encryption provides data confidentiality, it may also introduce potential issues such as limiting data owners' ability to query their data. The searchable attribute-based encryption (SABE) not only enables fine-grained access control in a dynamic large-scale environment but also allows for data searches on the ciphertext domain, making it an effective tool for cloud data sharing. Although SABE has become a research hotspot, existing schemes often have limitations in terms of computing efficiency on the client side, weak security of the ciphertext and the trapdoor. To address these issues, we propose an efficient server-aided ciphertext-policy searchable attribute-based encryption scheme (SA-CP-SABE). In SA-CP-SABE, the user's data access authority is consistent with the search authority. During the search process, calculations are performed not only to determine whether the ciphertext matches the keyword in the trapdoor, but also to assist subsequent user ciphertext decryption by reducing computational complexity. Our scheme has been proven under the random oracle model to achieve the indistinguishability of the ciphertext and the trapdoor and to resist keyword-guessing attacks. Finally, the performance analysis and simulation of the proposed scheme are provided, and the results show that it performs with high efficiency. [ABSTRACT FROM AUTHOR]

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on locally weighted projection regression (NSPC-LWPR). By projecting the input space into localized regions, constructing precise local models, and aggregating them through weighted summation, this approach handles the nonlinearity effectively. Additionally, it dynamically adjusts the control strategy based on online process data and model parameters, while eliminating the need for offline process data storage, greatly enhancing the adaptability and efficiency of the approach. The parameter determination criteria and theoretical analysis encompassing feasibility and stability assessments provide a robust foundation for the proposed approach. To illustrate its efficacy and feasibility, the proposed approach is applied to a continuous stirred tank heater (CSTH) benchmark system. Comparative results highlight its superiority over SPC and adaptive subspace predictive control (ASPC) methods, evident in enhanced tracking precision and predictive accuracy. Overall, the proposed NSPC-LWPR approach presents a promising solution for nonlinear control challenges in industrial process systems. [ABSTRACT FROM AUTHOR]

Featured Application: Increasing reproducibility for active learning-aided systematic screening is essential and our checklist can be used to evaluate reproducibility and data efficiency of software. In the screening phase of a systematic review, screening prioritization via active learning effectively reduces the workload. However, the PRISMA guidelines are not sufficient for reporting the screening phase in a reproducible manner. Text screening with active learning is an iterative process, but the labeling decisions and the training of the active learning model can happen independently of each other in time. Therefore, it is not trivial to store the data from both events so that one can still know which iteration of the model was used for each labeling decision. Moreover, many iterations of the active learning model will be trained throughout the screening process, producing an enormous amount of data (think of many gigabytes or even terabytes of data), and machine learning models are continually becoming larger. This article clarifies the steps in an active learning-aided screening process and what data is produced at every step. We consider what reproducibility means in this context and we show that there is tension between the desire to be reproducible and the amount of data that is stored. Finally, we present the RDAL Checklist (Reproducibility and Data storage for Active Learning-Aided Systematic Reviews Checklist), which helps users and creators of active learning software make their screening process reproducible. [ABSTRACT FROM AUTHOR]

This work presents a private consortium blockchain-based automotive data monetization architecture implementation using the Substrate blockchain framework. Architecture is decentralized where crowd-sourced data from vehicles are collectively auctioned ensuring data privacy and security. Smart Contracts and OffChain worker interactions built along with the blockchain make it interoperable with external systems to send or receive data. The work is deployed in a Kubernetes cloud platform and evaluated on different parameters like throughput, hybrid consensus algorithms AuRa and BABE, along with GRANDPA performance in terms of forks and scalability for increasing node participants. The hybrid consensus algorithms are studied in depth to understand the difference and performance in the separation of block creation by AuRa and BABE followed by chain finalization through the GRANDPA protocol. [ABSTRACT FROM AUTHOR]

Simple Summary: The volume of a pig, a new phenotype feature, can be used to estimate its weight due to its high correlation with body weight. The proportions of different body parts, such as the head and legs, can be determined through point cloud segmentation, providing new phenotype information for breeding pigs with smaller heads and stouter legs. However, due to the irregular shape and potential missing parts of the pig point cloud, it is challenging to form a closed surface for volume calculation. The study addresses this challenge by using an improved Poisson reconstruction algorithm, which provides smoother, more continuous, and complete reconstruction results, and its accuracy and reliability have been confirmed. The study also found that the correlation coefficient between pig body volume and weight was 0.95, indicating a strong relationship. This research could be valuable for improving the efficiency and accuracy of livestock weight estimation and breeding. Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95. [ABSTRACT FROM AUTHOR]

With superior computing power and efficient data collection capability, Internet of Medical Things (IoMT) significantly improves the accuracy and convenience of medical work. As most communications are over open networks, it is critical to encrypt data to ensure confidentiality before uploading them to cloud storage servers (CSSs). Public key encryption with keyword search (PEKS) allows users to search for specific keywords in ciphertext and plays an essential role in IoMT. However, PEKS still has the following problems: 1. As a semi-trusted third party, the CSSs may provide wrong search results to save computing and bandwidth resources. 2. Single-keyword searches often produce many irrelevant results, which is undoubtedly a waste of computing and bandwidth resources. 3. Most PEKS schemes rely on bilinear pairings, resulting in computational inefficiencies. 4. Public key infrastructure (PKI)-based or identity-based PEKS schemes face the problem of certificate management or key escrow. 5. Most PEKS schemes are vulnerable to offline keyword guessing attacks, online keyword guessing attacks, and insider keyword guessing attacks. We present a certificateless verifiable and pairing-free conjunctive public keyword searchable encryption (CLVPFC-PEKS) scheme. An efficiency analysis shows that the performance advantage of the new scheme is far superior to that of the existing scheme. More importantly, we provide proof of security under the standard model (SM) to ensure the reliability of the scheme in practical applications. [ABSTRACT FROM AUTHOR]

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models' data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements. [ABSTRACT FROM AUTHOR]

The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced. [ABSTRACT FROM AUTHOR]

For the actual collected point cloud data, there are widespread challenges such as semantic inconsistency, density variations, and sparse spatial distribution. A network called PointMM is developed in this study to enhance the accuracy of point cloud semantic segmentation in complex scenes. The main contribution of PointMM involves two aspects: (1) Multi-spatial feature encoding. We leverage a novel feature encoding module to learn multi-spatial features from the neighborhood point set obtained by k-nearest neighbors (KNN) in the feature space. This enhances the network's ability to learn the spatial structures of various samples more finely and completely. (2) Multi-head attention pooling. We leverage a multi-head attention pooling module to address the limitations of symmetric function-based pooling, such as maximum and average pooling, in terms of losing detailed feature information. This is achieved by aggregating multi-spatial and attribute features of point clouds, thereby enhancing the network's ability to transmit information more comprehensively and accurately. Experiments on publicly available point cloud datasets S3DIS and ISPRS 3D Vaihingen demonstrate that PointMM effectively learns features at different levels, while improving the semantic segmentation accuracy of various objects. Compared to 12 state-of-the-art methods reported in the literature, PointMM outperforms the runner-up by 2.3% in OA on the ISPRS 3D Vaihingen dataset, and achieves the third best performance in both OA and MioU on the S3DIS dataset. Both achieve a satisfactory balance between OA, F1, and MioU. [ABSTRACT FROM AUTHOR]

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask. [ABSTRACT FROM AUTHOR]

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data. [ABSTRACT FROM AUTHOR]

Clandestine assailants infiltrate intelligent systems in smart cities and homes for different purposes. These attacks leave clues behind in multiple logs. Systems usually upload their local syslogs as encrypted files to the cloud for longterm storage and resource saving. Therefore, the identification of pre-attack steps through log investigation is crucial for proactive system protection. Current methodologies involve system diagnosis using logs, often relying on datasets for feature training. Furthermore, the prevalence of mass encrypted logs in the cloud introduces a new layer of complexity to this domain. To tackle these challenges, we introduce CrptAC, a system for Multiple Encrypted Log Correlated Analysis, aimed at reconstructing attack chains to prevent further attacks securely. CrptAC initiates by searching and downloading relevant log files from encrypted logs stored in an untrusted cloud environment. Utilizing the obtained logs, it addresses the challenge of discovering event relationships to establish the attack provenance. The system employs various logs to construct event sequences leading up to an attack. Subsequently, we utilize Weighted Graphs and the Longest Common Subsequences algorithm to identify regular steps preceding an attack without the need for third-party training datasets. This approach enables the proactive identification of pre-attack steps by analyzing related log sequences. We apply our methodology to predict attacks in cloud computing and router breach provenance environments. Finally, we validate the proposed method, demonstrating its effectiveness in constructing attack steps and conclusively identifying corresponding syslogs. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms. [ABSTRACT FROM AUTHOR]

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the most unremitting and dynamic neurodegenerative human diseases. Various wearable IoT devices have emerged for detecting, diagnosing, and quantifying PD, predominantly utilizing inertial sensors and computational algorithms. However, their proliferation poses novel challenges concerning security, privacy, connectivity, and power optimization. Clinically, continuous monitoring of patients' motor function is imperative for optimizing Levodopa (L-dopa) dosage while mitigating adverse effects and motor activity decline. Tracking motor function alterations between visits is challenging, risking erroneous clinical decisions. Thus, there is a pressing need to furnish medical professionals with an ecosystem facilitating comprehensive Parkinson's stage evaluation and disease progression monitoring, particularly regarding tremor and bradykinesia. This study endeavors to establish a holistic ecosystem centered around an energy-efficient Wi-Fi-enabled wearable bracelet dubbed A-WEAR. A-WEAR functions as a data collection conduit for Parkinson's-related motion data, securely transmitting them to the Cloud for storage, processing, and severity estimation via bespoke learning algorithms. The experimental results demonstrate the resilience and effectiveness of the suggested technique, with 86.4% accuracy for bradykinesia and 90.9% accuracy for tremor estimation, along with good sensitivity and specificity for each scoring class. The recommended approach will support the timely determination of the severity of PD and ongoing patient activity monitoring. The system helps medical practitioners in decision making when initially assessing patients with PD and reviewing their progress and the effects of any treatment. [ABSTRACT FROM AUTHOR]

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)—a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration. [ABSTRACT FROM AUTHOR]

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity. [ABSTRACT FROM AUTHOR]

Farmland abandonment monitoring is one of the key aspects of land use and land cover research, as well as being an important prerequisite for ecological environmental protection and food security. A Normalized Difference Vegetation Index (NDVI) time series analysis is a common method used for farmland abandonment data extraction; however, extracting this information using high-resolution data is still difficult due to the limitations caused by cloud influence and data of low temporal resolution. To address this problem, this study used STARFM for GF-6 and Landsat 8 data fusion to enhance the continuity of high-resolution and cloudless images. A dataset was constructed by combining the phenological cycle of crops in the study area and then extracting abandoned farmland data based on an NDVI time series analysis. The overall accuracy of the results based on the NDVI time series analysis using the STARFM-fused dataset was 93.42%, which was 15.5% higher than the accuracy of the results obtained using only GF-6 data and 28.52% higher than those obtained using only Landsat data. Improvements in accuracy were also achieved when using SVM for time series analysis based on the fused dataset, indicating that the method used in this study can effectively improve the accuracy of the results. Then, we analyzed the spatial distribution pattern of abandoned farmland by extracting the results and concluded that the abandonment rate increased with the increase in the road network density and decreased with the increase in the distance to residential areas. This study can provide decision-making guidance and scientific and technological support for the monitoring of farmland abandonment and can facilitate the analysis of abandonment mechanisms in the study area, which is conducive to the sustainable development of farmland. [ABSTRACT FROM AUTHOR]

BIM collaborative design involves numerous participants from various specialties who create and share vast amounts of design data at different design stages to ensure the efficient transmission of design data between these specialties. It is imperative for the BIM collaborative design platform to guarantee the security of design data and effectively trace any instances of malicious leakage or tampering. Therefore, this paper proposes a traceable ciphertext-policy attribute-based encryption scheme (TCP-ABE) that formulates a dynamic data access control mechanism based on different participants and effectively tracks malicious users in the event of risks such as tampering, theft, and unauthorized access to BIM data. In this scheme, the user's identity information is embedded into their private key as a key component, ensuring that only users who satisfy the access policy can decrypt it. The compromised private key allows for tracing of the user's identity. Additionally, Linear Secret Sharing Scheme (LSSS) is employed as the access structure with the user's attribute set divided into an attribute name set and an attribute value set to protect privacy by concealing the latter within the access policy. Furthermore, the scheme integrates blockchain with cloud storage as a trusted third-party storage mechanism to ensure data integrity. Finally, the TCP-ABE scheme is comprehensively evaluated by comparing its strengths and weaknesses with other algorithms. This evaluation includes a theoretical analysis of functional and computational time overhead aspects, as well as an experimental analysis of initialization time, data encryption time, and data decryption time. The scheme exhibits excellent performance across all stages and encompasses the most comprehensive functionalities, as demonstrated by the comparative analysis and experimental results. [ABSTRACT FROM AUTHOR]

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model's capabilities to encompass unstructured data classification and grading, broadening the scope of application. [ABSTRACT FROM AUTHOR]

Blockchain technology has been successfully applied in recent years to promote the immutability, traceability, and authenticity of previously collected and stored data. However, the amount of data stored in the blockchain is usually limited for economic and technological issues. Namely, the blockchain usually stores only a fingerprint of data, such as the hash of data, while full, raw information is stored off-chain. This is generally enough to guarantee immutability and traceability, but misses to support another important property, that is, data availability. This is particularly true when a traditional, centralized database is chosen for off-chain storage. For this reason, many proposals try to properly combine blockchain with decentralized IPFS storage. However, the storage of data on IPFS could pose some privacy problems. This paper proposes a solution that properly combines blockchain, IPFS, and encryption techniques to guarantee immutability, traceability, availability, and data privacy. [ABSTRACT FROM AUTHOR]

Robinia pseudoacacia is widely planted in the Loess Plateau as a major soil and water conservation tree species because of its dense canopy, complex structure, and strong soil and water conservation ability. The precise measurement of small-scale locust forest biomass is crucial to monitoring and evaluating the carbon sequestration functions of soil and water conservation vegetation. This study focuses on an artificial locust forest planted in the early 1990s in Caijiachuan Basin, Jixian County, Shanxi Province. A drone equipped with LiDAR was used to obtain point cloud data and generate a canopy height model. A watershed segmentation algorithm was used to identify tree vertices and extract individual trees. A relationship model between tree height, diameter at breast height, and biomass, combined with sample survey data, was established to explore the spatial distribution of biomass in the artificial locust forest at the level of the entire basin. The results show the following: (1) the structural parameters of locust extracted using UAV point cloud data have a good degree of fit and accuracy, and the recall rate is 72.7%; (2) the average error rate of the extracted maximum tree height value of locust is 7%, that of the minimum tree height value is 14%, and that of the average tree height value is 18%; (3) the average error rate of the extracted maximum diameter at breast height of locust is 15%, that of the minimum diameter at breast height is 37%, and that of the average diameter at breast height is 36%; and (4) the average error rate of the biomass estimation of locust calculated using point cloud data is 16.0%. [ABSTRACT FROM AUTHOR]

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage. [ABSTRACT FROM AUTHOR]

The big data era demands an efficient and permanent data storage technology with the capacity of PB to EB scale. Optical data storage (ODS) offers a good candidate for long-lifetime storage, as the developing far-field super-resolution nanoscale writing technology improves its capacity to the PB scale. However, methods to efficiently read out this intensive ODS data are still lacking. In this paper, we demonstrate a sub-diffraction readout method based on polarization modulation, which experimentally achieves the sub-diffraction readout on Disperse Red 13 thin film with a resolution of 500 nm, exceeding the diffraction limit by 1.2 times (NA = 0.5). Differing from conventional binary encoding, we propose a specific polarization encoding method that enhances the capacity of ODS by 1.5 times. In the simulation, our method provides an optical data storage readout resolution of 150 nm, potentially to 70 nm, equivalent to 1.1 PB in a DVD-sized disk. This sub-diffraction readout method has great potential as a powerful readout tool for next-generation optical data storage. [ABSTRACT FROM AUTHOR]

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene. [ABSTRACT FROM AUTHOR]

This paper presents the cloud vendor lock-in prediction framework (CVL), which aims to address the challenges that arise from vendor lock-in in cloud computing. The framework provides a systematic approach to evaluate the extent of dependency between service providers and consumers and offers predictive risk analysis and detailed cost assessments. At the heart of the CVL framework is the Dependency Module, which enables service consumers to input weighted factors that are critical to their reliance on cloud service providers. These factors include service costs, data transfer expenses, security features, compliance adherence, scalability, and technical integrations. The research delves into the critical factors that are necessary for dependency calculation and cost analysis, providing insights into determining dependency levels and associated financial implications. Experimental results showcase dependency levels among service providers and consumers, highlighting the framework's utility in guiding strategic decision-making processes. The CVL is a powerful tool that empowers service consumers to proactively navigate the complexities of cloud vendor lock-in. By offering valuable insights into dependency levels and financial implications, the CVL aids in risk mitigation and facilitates informed decision-making. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients' real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost. [ABSTRACT FROM AUTHOR]