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Blockchain, essentially a distributed and immutable database, has emerged as a potential solution to governance problems of accountability, transparency, and trust in the construction industry. In terms of construction quality management (CQM) applications, previous works mainly took a technical perspective, and little is known about the potential of blockchain-based governance for quality opportunism. Moreover, we have seen very few successful blockchain implementations in construction practices. Stakeholders meet the trade-off between potential benefits and risks in blockchain adoption decisions. Previous works mainly focused on barrier analysis, neglecting the dynamic interactions of stakeholders' strategic behaviors on adopting blockchains. Against this backdrop, this research aims to systematically analyze blockchain impacts on CQM from a governance perspective and then investigate the owner and the main contractors' strategic behaviors (e.g., adopting blockchains in CQM or not). The agency theory was used as the theoretical lens to examine how blockchain curbs contractors' quality opportunistic behaviors as a governance mechanism. An evolutionary game theory model was developed to simulate adoption decision interactions between the owner and the main contractor. Finally, policy suggestions were highlighted for policymakers. This research could be one of the first studies examining blockchain potential to CQM from an opportunism governance perspective. Such discussions could inspire more discussions on blockchain-based governance and broaden researchers' and practitioners' understandings of blockchain impacts. The proposed policy suggestions from the equilibrium analysis could facilitate blockchain diffusion in the construction industry. [ABSTRACT FROM AUTHOR]

Background: A blockchain can be described as a distributed ledger database where, under a consensus mechanism, data are permanently stored in records, called blocks, linked together with cryptography. Each block contains a cryptographic hash function of the previous block, a timestamp, and transaction data, which are permanently stored in thousands of nodes and never altered. This provides a potential real-world application for generating a permanent, decentralized record of scientific data, taking advantage of blockchain features such as timestamping and immutability. Implementation: Here, we propose INNBC DApp, a Web3 decentralized application providing a simple front-end user interface connected with a smart contract for recording scientific data on a modern, proof-of-stake (POS) blockchain such as BNB Smart Chain. Unlike previously proposed blockchain tools that only store a hash of the data on-chain, here the data are stored fully on-chain within the transaction itself as "transaction input data", with a true decentralized storage solution. In addition to plain text, the DApp can record various types of files, such as documents, images, audio, and video, by using Base64 encoding. In this study, we describe how to use the DApp and perform real-world transactions storing different kinds of data from previously published research articles, describing the advantages and limitations of using such a technology, analyzing the cost in terms of transaction fees, and discussing possible use cases. Results: We have been able to store several different types of data on the BNB Smart Chain: raw text, documents, images, audio, and video. Notably, we stored several complete research articles at a reasonable cost. We found a limit of 95KB for each single file upload. Considering that Base64 encoding increases file size by approximately 33%, this provides us with a theoretical limit of 126KB. We successfully overcome this limitation by splitting larger files into smaller chunks and uploading them as multi-volume archives. Additionally, we propose AES encryption to protect sensitive data. Accordingly, we show that it is possible to include enough data to be useful for storing and sharing scientific documents and images on the blockchain at a reasonable cost for the users. Conclusion: INNBC DApp represents a real use case for blockchain technology in decentralizing biomedical data storage and sharing, providing us with features such as immutability, timestamp, and identity that can be used to ensure permanent availability of the data and to provide proof-of-existence as well as to protect authorship, a freely available decentralized science (DeSci) tool aiming to help bring mass adoption of blockchain technology among the scientific community. [ABSTRACT FROM AUTHOR]

The act of gathering, looking over, and analyzing a lot of data to find patterns, insights, and market trends that can help businesses make more effective choices is known as big data analysis (BDA). Quick and effective access to this data allows businesses to be flexible in developing strategies to hold onto their competitive edge. To analyze massive amounts of data quickly through parallel processing, the structure of the Hadoop software employs the MapReduce methodology. Computational solid resources are necessary for BDA, although they are not always available. Developing new clustering techniques that could handle this kind of data processing became crucial. Therefore, in this research, we presented a novel, effective fuzzy-based Improved Multiview Fuzzy C-Means Algorithm (IMV-FCA) to boost the clustering strategy. To summarize, fuzzy-based IMV-FCA clustering presents the ensemble of the MobileNet V2 model, and three-layered stacked Bidirectional LSTM (MVSBiLSTM) to increase computing speed and effectiveness. It also presents a function that calculates the separation among the cluster center and the particular instance, to assist with better clustering. By simulating shared memory space and parallelizing on the framework known as MapReduce on the Hadoop cloud computing platform, the distributed database is utilized to improve the method’s effectiveness while reducing its time complexities. The experimental investigation was conducted on existing approaches, and the proposed approach was analyzed using three standard datasets. While differentiating from existing approaches, the presented approach yields greater performances in terms of various metrics. [ABSTRACT FROM AUTHOR]

With the progress of society, the supply of electricity has become an inevitable choice for the development of all walks of life. The stability, safety, and economic dispatch of the electric energy system directly impact the entire country's economic development, from national defense, military, and even people's daily life, all closely associated with the smooth running of the network. In the aspect of power grid planning and operation, the economic security dispatching problem of power system is a very typical optimization problem. The problem is how to maximize the operating cost of the system under the premise of satisfying the system's load limit and safety and stability. Due to the rapid development of power technology, the research on power system security and economic planning is essential. As an important means to promote China's energy structure adjustment and promote the supply side structural reform, the regional spot electricity market is playing an increasingly important role in building a resource optimization allocation mechanism, improving China's energy resource allocation efficiency, and promoting social and economic development. However, the development of China's regional spot power market is still in its infancy, and the transaction price of the regional spot power market is quite different from that of other countries. It was given these problems, this article discusses the use of particle swarm algorithms for secure and economic scheduling of power systems in the context of securely reading blockchains and distributed data. The research results showed that: The introduction of environmental pollution penalty programs improved the priority dispatch of wind and photovoltaic power when considering environmental penalty programs and backup penalties for wind and photovoltaic power. The setting of the coefficient of the environmental penalty term depended on the designer's emphasis on the priority scheduling of wind power and photovoltaic (PV) power generation (PG), and the standby capacity penalty clause achieved reasonable dispatch and utilization of wind power and PV PG, which reduced the dramatic fluctuations and intermittent volume of the system, and facilitated safe and consistent operation of the network. The research of this paper shows the positive relationship between the blockchain and distributed data security reading algorithm and the clearing method of regional spot power market, and points out a new method for its development. [ABSTRACT FROM AUTHOR]

Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm's longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method's performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

The main advantage of Vehicular Adhoc Networks being an inherent part of Intelligent Transportation Systems is to ensure the safety and comfort of commuters on the roads. In VANETs, due to the wide open nature of wireless communications, data transmission has been subjected to variety of security and privacy threats which causes disruptions in the network. Hence data has to be authenticated prior to communication. To address this problem enhanced elliptic curve cryptography based certificate-less signature aggregation scheme has been proposed to ensure full privacy preservation and to provide efficient security to the VANETs. Through formal and informal security analysis the proposed scheme has been tested and proven to be secure under suppositions made using Diffie-Hellman assumptions by using Random Oracle Model. The proposed scheme is implemented using MIRACL C + + library and by using Veins Integrated Simulation Framework. The proposed scheme has been analyzed and compared with the existing standards which prove that the proposed scheme is efficient in terms of computation cost and communication costs. The major novelty of the proposed scheme is the use of aggregation and point addition which does not cause delay in verification and also reduces overhead in the road-side units by employing elliptic curve cryptography based distributed data authentication scheme. Hence, the traditional problems of certificate management and key escrow problem is solved by the proposed scheme in an efficient manner. This makes the proposed scheme to be much suitable for high volatile and resource-constrained vanet environments. [ABSTRACT FROM AUTHOR]

Three-dimensional resistivity/chargeability tomography based on distributed data acquisition technology is likely to provide abundant information for mineral exploration. To realize true 3D tomography, establishing transmitter sources with different injection directions and collecting vector signals at receiver points is necessary. We implemented 3D resistivity/ chargeability tomography to search for new ore bodies in the deep and peripheral areas of Huaniushan, China. A distributed data acquisition system was used to form a vector receiver array in the survey area. First, by using the expanding gradient array composed of 11 pairs of transmitter electrodes, we quickly obtained the 3D distributions of the resistivity and chargeability of the whole area. Based on the electrical structure and geological setting, a NE-striking potential area for mineral exploration was determined. Next, a pole–dipole array was employed to depict the locations and shapes of the potential ore bodies in detail. The results showed that the inversion data for the two arrays corresponded well with the known geological setting and that the ore veins controlled by boreholes were located in the low-resistivity and high-chargeability zone. These results provided data for future mineral evaluation. Further research showed that true 3D tomography has obvious advantages over quasi-3D tomography. The expanding gradient array, characterized by a good signal strength and field efficiency, was suitable for the target determination in the early exploration stage. The pole–dipole array with high spatial resolution can be used for detailed investigations. Choosing a reasonable data acquisition scheme is helpful to improve the spatial resolution and economic efficiency. [ABSTRACT FROM AUTHOR]

Whole-stand Models (WSM) have always been fitted with permanent plot data organised in a sequential age-matched database, i.e., i and i+1, where i = 1, 2, ... N plot measurements. The objectives of this study were (1) to evaluate the statistical efficiency of a monthly distributed data structure by fitting the models of Clutter (1963), Buckman (1962) in the version modified by A. L. da Silva et al. (2006), and deep learning, and (2) to evaluate the possibility of gaining accuracy in yield projections made from an early age to harvest age of eucalypt stands. Three alternatives for organizing the data were analyzed. The first is with data paired in sequential measurement ages, i.e., i and i+1, where i = 1, 2, ... N plot measurements. In the second, all possible measurement intervals for each plot were considered, i.e., i, i+1; i, i+2; ...; i, N; i+1, i+2; ..., N-1, N. The third has data paired by month (j), always with an interval of one month, i.e., j, j+1; j+1, j+2; j+M-1, M, where M is the stand age of the plot measurement in months. This study shows that the accuracy and consistency of the projections depend on the organization of the monthly distributed data, except for the Clutter model. A better alternative to increasing the statistical assumptions of the forecast from early to harvest age is based on a monthly distributed data structure using a deep learning method. [ABSTRACT FROM AUTHOR]

The application of IoT covers various domains, including smart cities, smart homes, e‐health, autonomous vehicles and so forth. These are powered by IoT networks which consist of a collection of heterogeneous devices. These devices collect and analyze personal data and sometimes make decisions for us. Since IoTs are involved in the decision‐making process, the authenticity and privacy of such networks are critical. In this article, authenticated devices configuration protocol (ADCP) is proposed to handle the authentication and construct a secure overlay network in the existing IoT networks; authenticated device transmission protocol is also proposed to secure the communication within the overlay network. Besides the authentication, ADCP can also mitigate the zero‐day attack and achieve zero the zero‐day attack can also be mitigated and achieved 0 round trip time key exchange. These protocols utilize a distributed blockchain database to store the authentication records optimized for data integrity. These properties guarantee the integrity of the authentication records. Both protocols are compatible with the existing communication protocols and do not require reprogramming the current software. The formal analysis of the proposed scheme has proven its resilience against various possible attacks. In addition, a stochastic threat model is considered and numerical results give solid evidence of security enhancement. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (I-IoT) has become an emerging driver to operate industrial systems and a primary empowerer to future industries. With the advanced technologies such as artificial intelligence (AI) and machine learning widely used in IoT, the Industrial IoT is also witnessing changes driven by new technologies. Generally, AI technologies require centralized data collection and processing to learn from the data to obtain viable models for application. In industrial IoT, data security and privacy problems associated with reliable and interconnected end devices are being faced and reliable solutions are urgently needed. A trusted execution environment in IoT devices is gradually becoming a feasible approach, and a distributed solution is a natural choice for artificial intelligence technologies in I-IoT. Moreover, Federated Learning as a distributed machine learning paradigm with privacy-preserving properties can be used in I-IoT. This paper introduces a feasible secure data circulation and sharing scheme for I-IoT devices in a trusted implementation platform by employing federated learning. The suggested framework has proved to be efficient, reliable, and accurate. [ABSTRACT FROM AUTHOR]

Distributed database functionality depends on sites responsible for the allocation of fragments. The aims of the data allocation problem (DAP) are to achieve the minimum execution time and ensure the lowest transaction cost of queries. The solution for this NP-hard problem based on numerical methods is computationally expensive. Despite the success of such heuristic algorithms as GA and PSO in solving DAP, the initial control parameters tuning, the relatively high convergence speed, and hard adaptations to the problem are the most important disadvantages of these methods. This paper presents a simple well-formed greedy algorithm to optimize the total transmission cost of each site-fragment dependency and each inner-fragment dependency. To evaluate the effect of the proposed method, more than 20 standard DAP problems were used. Experimental results showed that the proposed approach had better quality in terms of execution time and total cost. [ABSTRACT FROM AUTHOR]

Database management systems (DBMS) are essential systems of software technologies for multi-level and multi-angled operations with the utmost security. Currently, its form of distributed database (DDB) is very useful and popular in communication networks. The current write-up is aimed at investigating important security aspects and issues, exploring some major challenges pertinent to the security of DDB, finding solutions to the same, and eventually talking about some future opportunities through promoting research towards curbing these security- related points. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last ten years. It comprises the application of DDBMS, major pertinent issues within its range of investigation, and a conclusion. [ABSTRACT FROM AUTHOR]

The median path problem (min-sum criterion) is a common problem in graph theory and tree networks. This problem is open to study because its applications are growing and extending in different fields, such as providing insight for decision-makers when selecting the optimal location for non-emergency services, including railroad lines, highways, pipelines, and transit routes. Also, the min-sum criterion can deal with several networks in different applications. The location problem has traditionally been concerned with the optimal location of a single-point facility at either a vertex or along an edge in a network. Recently, numerous investigators have investigated this classic problem and have studied the location of many facilities, such as paths, trees, and cycles. The concept of the median, which measures the centrality of a vertex in a graph, is extended to the paths in a graph. In this paper, we consider the problem of locating path-shaped facilities on a tree network. A new modified and improved algorithm for finding a median single path facility of a specified length in a tree network is proposed. The median criterion for optimality considers the sum of the distances from all vertices of the tree to the path facility. This problem under the median criterion is called the ℓ-core problem. The distance between any two vertices in the tree is equal to the length of the unique path connecting them. This location problem usually has applications in distributed database systems, pipelines, the design of public transportation routes, and communication networks. [ABSTRACT FROM AUTHOR]

Blockchain has become a widely used information system technology recently because of its effectiveness as an intermediary-free platform. While the use of blockchain in various fields, such as finance, supply chains, healthcare, education, and energy consumption, is increasingly enabling the development of Internet-enabled "distributed databases," there are not many exploratory studies available to provide an understanding of how the field is progressing. Therefore, it is imperative to explore the status quo of blockchain technology in the finance sector, particularly highlighting how blockchain architectures can aid the finance sector to gain competitive advantage. This systematic literature review analyzes the content of the 50 most relevant articles and professional industry reports through peer-reviewed relevant academic literature in the finance sector from 2008 to 2022 to identify several possible features of blockchain research in the financial sector. This study highlighted the dimensions of blockchain technology, blockchain in finance, its competitive advantages, the current status of finance, and various challenges that keep the implementation of blockchain-based financial information systems at the initial stage. We identified three main areas that require research attention in order for blockchain technology to become the "next-generation networks" that will revolutionize the financial sector. [ABSTRACT FROM AUTHOR]

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases. [ABSTRACT FROM AUTHOR]

Using structural health monitoring (SHM) techniques, Brillouin optical time-domain analysis (BOTDA) sensors can be mounted along the main box girder entire length of a long-span suspension bridge, and the high-density measured points strain monitoring data can be obtained. However, insufficient research has been conducted on accurately diagnosing the structural condition of a long-span suspension bridge by using the abovementioned strain monitoring data. To address this issue, a cross-diagnosis method that determines the structural condition of long-span suspension bridges based on the distributed strain data spatial window is proposed in this study. First, the distributed strain data spatial window based on a long-span suspension bridge structural symmetry is defined. Then, a method that divides the distributed strain data of the bridge main box girder into different spatial windows using mutual information between the strain data from BOTDA sensors is presented. The special symmetry of the environmental temperature effect on the spatial window structural performance is carried out to separate the temperature effect from the strain monitoring data; this process can effectively reduce the interference of ambient temperature on the results of the structural condition diagnosis. Second, using a convolutional neural network, a diagnosis index of the structural condition is generated by using the correlation model between the high-density measured points and the distributed strain data belonging to one whole spatial window. Regarding one spatial window, the proposed diagnosis index can effectively reflect the variation in the distributed strain correlation model caused by the damaged condition of the long-span suspension bridge to achieve cross-diagnosis of the structural condition of the bridge. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation using strain monitoring data obtained from a real bridge. [ABSTRACT FROM AUTHOR]

In recent years, BCT has garnered significant attention from researchers worldwide. The technology in question is a distributed database system characterised by its decentralised nature and lack of reliability. BCT has been widely adopted by numerous governments and scholars across various sectors for a number of years. Blockchain technology also involves highly innovative and advanced concepts. Given the increasing interest among scholars in the academic community regarding the agrifood supply chain, the objective of this study was to investigate BCT and its potential for application in the fields of food and agriculture. This research paper presents a bibliometric analysis of articles on the utilisation of BCT in the fields of food and agriculture. This study discusses scholarly articles that have been published in esteemed academic journals and conferences. Through our bibliometric analysis, we aimed to discern the recurring trends and themes within the research on BCT in relation to agrifood systems. Furthermore, this study examines a diverse array of research domains, prominent scholarly publications, leading publishing platforms, prominent funding institutions, and the prospective trajectory of future research. This study also presents the prominent patterns and themes within this field through an analysis of the most influential scholarly articles, authors, countries, and keywords found in the existing literature. Hence, this research employed various analytical techniques, including analyzing the co-occurrence of author keywords, bibliographic coupling analysis, network view map analysis, and co-citation analysis. This study holds promise as a valuable learning resource for aspiring researchers seeking to acquire compelling and pertinent information about research outcomes from studies on the utilisation of BCT in the field of smart agriculture. [ABSTRACT FROM AUTHOR]

Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user's activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively. [ABSTRACT FROM AUTHOR]

Preserving privacy and security plays a key role in allowing each component in the healthcare system to access control and gain privileges for services and resources. Over recent years, there have been several role-based access control and authentication schemes, but we noticed some drawbacks in target schemes such as failing to resist well-known attacks, leaking privacy-related information, and operational cost. To defeat the weakness, this paper proposes a secure electronic healthcare record scheme based on Schnorr Signcryption, crypto hash function, and Distributed Global Database (DGDB) for the healthcare system. Based on security theories and the Canetti-Krawczyk model (CK), we notice that the proposed scheme has suitable matrices such as scalability, privacy preservation, and mutual authentication. Furthermore, findings from comparisons with comparable schemes reveal that the suggested approach provides greater privacy and security characteristics than the other schemes and has enough efficiency in computational and communicational aspects. [ABSTRACT FROM AUTHOR]

When deciding against outsourcing their data to the cloud, organizations often point to security as the primary reason. If cloud is not used as a passive storage only, but rather both the data and the code required for their processing are being outsourced, then the data privacy may get compromised in two ways: (i) in the storage if not being encrypted and (ii) during the processing through various execution-level attacks. Encrypting the data before outsourcing enhances their security while in the storage, but disables their processing in the cloud. On the other hand, if a cloud has the ability to decrypt the data before processing, then they remain vulnerable during the execution. In this paper, we present a paradigm for outsourcing both the data and the code to the cloud in a way that preserves data privacy, while still enabling their processing outside the organization. The paradigm leverages constraint-based data and code fragmentation and deploys these fragments to multiple independent computer clouds. We introduce several architectural patterns for secure computation in a multi-cloud environment, demonstrate the paradigm use, and examine introduced performance penalty on a simple application. [ABSTRACT FROM AUTHOR]

The construction of systems supporting spatial data has experienced great enthusiasm in the past, due to the richness of this type of data and their semantics, which can be used in the decision-making process in various fields. Thus, the problem of integrating spatial data into existing databases and information systems has been addressed by creating spatial extensions to relational tables or by creating spatial data warehouses, while arranging data structures and query languages by making them more spatially-aware. With the advent of Big Data, these conventional storage and spatial representation structures are becoming increasingly outdated, and required a new organization of spatial data. Approaches based on distributed storage and data lakes have been proposed, to integrate the complexity of spatial data, with operational and analytical systems which unfortunately quickly showed their limits. Recently the concept of lakehouse was introduced in order to integrate, among other things, the notion of reliability and ACID properties to the volume of data to be managed. This new data architecture is a combination of governed and reliable Data Warehouses and flexible, scalable and cost-effective Data Lakes. In this paper, we present how traditional approaches of spatial data management in the context of spatial big data have quickly shown their limits. We present a literature overview of these approaches, and how they led to the Data LakeHouse. We detail how the Lakehouse paradigm can be used and extended for managing spatial big data, by giving the different components and best practices for building a spatial data LakeHouse architecture optimized for the storage and computing over spatial big data. • Limitations of Data Warehouses and Data Lakes for Spatial Big Data. • Characteristics and Architecture of the Data LakeHouse. • Overview of Three Major Open Source LakeHouse Systems. • Challenges in Using Data LakeHouse for Spatial Big Data. • Optimized Data LakeHouse Architecture for Spatial Big Data. [ABSTRACT FROM AUTHOR]

Multiway spatial joins are a commonly occurring and fundamental type of query for spatial data processing. This article presents models and algorithms to schedule this type of query in distributed database systems while attempting to strike a balance between makespan and communication costs. We propose three algorithms based on combinatorial optimization methods: the well-known linear relaxation technique of rounding a solution generated by linear programming (LP), a more sophisticated Lagrangian Relaxation method (LR), as well as a greedy heuristic (GR) for baseline comparison. Our evaluation shows that a schedule built using GR consumes, on average, 22% more processing and communication resources than a more elaborate schedule constructed via the LR method, when scheduling a query for 64 machines. The schedule provided by LR is also, on average, an order of magnitude closer to the optimal schedule for a query compared to GR. We show that scheduling Gigabyte-size multiway queries before execution can reduce its processing time by an order of magnitude compared to state-of-the-art frameworks for spatial data processing that do not have this capability, and can significantly reduce the amount of shuffled data in the network. [ABSTRACT FROM AUTHOR]

Determining the timing and drivers of Pleistocene hydrological change in the interior of South Africa is critical for testing hypotheses regarding the presence, dynamics, and resilience of human populations. Combining geological data and physically based distributed hydrological modeling, we demonstrate the presence of large paleolakes in South Africa's central interior during the last glacial period, and infer a regional-scale invigoration of hydrological networks, particularly during marine isotope stages 3 and 2, most notably 55 to 39 ka and 34 to 31 ka. The resulting hydrological reconstructions further permit investigation of regional floral and fauna responses using a modern analog approach. These suggest that the climate change required to sustain these water bodies would have replaced xeric shrubland with more productive, eutrophic grassland or higher grass-cover vegetation, capable of supporting a substantial increase in ungulate diversity and biomass. The existence of such resource-rich landscapes for protracted phases within the last glacial period likely exerted a recurrent draw on human societies, evidenced by extensive pan-side artifact assemblages. Thus, rather than representing a perennially uninhabited hinterland, the central interior's underrepresentation in late Pleistocene archeological narratives likely reflects taphonomic biases stemming from a dearth of rockshelters and regional geomorphic controls. These findings suggest that South Africa's central interior experienced greater climatic, ecological, and cultural dynamism than previously appreciated and potential to host human populations whose archaeological signatures deserve systematic investigation. [ABSTRACT FROM AUTHOR]

This paper focuses on highlighting the problems that are associated with the absence of privacy and security of medical records in a healthcare system. It seeks to bridge the gap between the currently used security protocols in the management of health information, and encryption algorithms that should be used. Extant health information systems have always been developed with conventional databases. With all the privileges to read, write and execute assigned to the administrator, who has centralised control over all medical records, there is the likelihood of the misuse, distortion and loss of such records in the event that the administrator becomes compromised or inadvertent system failure. To solve this problem, the use of decentralised and distributed databases becomes paramount. Blockchain technology has recently received much attention due to its ability to permit a peer-to-peer network with distributed databases that can be stored locally on each node in the network. Subsequently, all updates on records in a database are communicated to all participating parties, hence addressing the problem of centralised control. In this paper, we propose a health information system on a blockchain to create a trust-free system for both health personnel and patients. From the results obtained, we achieved the decentralisation of the medical records’ database to enhance the security and privacy of data on the modeled peer-to-peer network. [ABSTRACT FROM AUTHOR]

Deepening financial integration is important for developing economies especially in Africa, that accounts for less than 2% of the world’s market capitalization. Financial integration facilitates risk sharing, aids the formation of larger markets which is beneficial to the firms and smoothens consumption inter-temporally. Financial integration will be enhanced through a well-developed financial sector that is all-inclusive of the economic agents in access to financial services at a low cost and at the most convenient time. The access to funds in an integrated market is further determined by the extent of financial knowledge acquired by the economic agents with respect to basic numeracy, interest compounding, understanding inflation and risk diversification. This paper examines the effect of increasing access to financial services and the level of knowledge of financial products on the financial integration of West African Monetary Zone. The ratio of total capital flows to GDP and interest rates on public debt were used in measuring financial integration while the measures of financial inclusion include the number of commercial bank branches, number of ATM machines available, Savings, number of account holders and credit to private sector. Data covering the period 1980 – 2020 were employed. Unit root test using Levin, Lin and chu t-test method and Im, et al statistics methods were employed while the cointegration test was conducted using the Wald t-test before the panel autoregressive distributed lag model estimation technique was used to estimate the models. The unit root test results revealed that three out of the ten variables were stationary at level whiles others were stationary at first difference. The Wald cointegration test shows that the two equations are cointegrated. The results from the study show that increasing the number of bank branches; the number of account holders; savings; credit to private sector and improvement in the level of financial knowledge with respect to financial services deepen financial integration of the West African Monetary Zone. The policy implication of the paper is that each economy’s monetary authority and financial service providers must continue to advocate and institutionalise programmes that will deepen access to and knowledge of financial services, this will improve the extent of financial integration. [ABSTRACT FROM AUTHOR]

Characterising spatial variability, which is of utter importance in inspection and maintenance strategies, requires comprehensive spatially distributed databases. However, in real practice, spatially distributed inspection is costly and could damage the structure if a large number of destructive tests are carried out. Therefore, the first objective of this work is to propose a methodology to extract as much informations as possible from available spatially distributed databases, in order to characterise the spatial correlation. Moreover, a preventive maintenance strategy should be supported by deterioration models able to propagate uncertainty and spatial variability. Then, the second objective of the paper is to evaluate the ability of these models to propagate uncertainties and spatial variability. The methodology is illustrated with data collected through destructive tests in a concrete wall exposed to carbonation. The database encompasses information about the concrete porosity, saturation degree, density, and carbonation depth. Recommendations are hence provided in this work for the choice of input parameters that should be modelled as random fields. These recommendations were applied and then confirmed by comparing measured and modelled spatially distributed carbonation depths. The results highlight that uncertainties in measurements and statistical uncertainties have significant impact when dealing with spatial variability. [ABSTRACT FROM AUTHOR]

objective: The Video Head Impulse Test (V-HIT) is an easy-to-use, objective test that allows the evaluation of the six semicircular canals (CSC) and the visualization of the saccades that occur during the vestibulo-ocular reflex (VOR) testing. The aim of the study was to compare V-HIT data obtained from healthy individuals between the ages of 18 and 30 and to establish VOR normative data based on equally distributed gender group. Method: A hundred and thirty healthy people (65F-65E) between the ages of 18 and 30 got the V-HIT. The head was turned 30 degrees forward for horizontal channels and 30-45 degrees right-left to record a total of five answers in each vertical channel. All test participants were retested 30 minutes later for test reliability. We compared VOR gains across all channels, independent of gender. Results: A statistically significant difference was found in the VOR gains of all individuals in the anterior and posterior canals of the rightleft ear (p<0.05), and in the right anterior canal in women according to gender (p<0.05). A positive, high level correlation was obtained in all channels with the test retest results. Conclusions: In contrast to previous research, this one features a higher proportion of participants aged 18 to 30 and an even distribution of men and women throughout the age spectrum. The strong reliability correlation implies that the values can serve as a reference. [ABSTRACT FROM AUTHOR]

We investigate the in-network processing of a skyline join query in wireless sensor networks (WSNs). While much research was conducted on processing skyline queries in WSNs, skyline join queries were dealt with only in traditional centralized or distributed database environments. However, such techniques cannot be applied to WSNs. Carrying out join filtering, as well as skyline filtering using them in WSNs, is infeasible due to limited memory in senor nodes and to excessive energy consumption in wireless communications. In this paper, we propose a protocol to process a skyline join query in WSNs energy efficiently with only a small amount of memory in each sensor node. It uses a synopsis of skyline attribute value ranges, which is a very compact data structure. The range synopsis is used both in the search of anchor points for skyline filtering and in 2-way semijoins for join filtering. We describe the structure of a range synopsis and present our protocol. To optimize our protocol, we solve some optimization problems. Through implementation and a set of detailed simulations, we show the effectiveness of our protocol. The range synopsis is confirmed to be compact enough for our protocol to work well with the limited memory and energy in each sensor node. For the correlated and random distributions, our protocol significantly outperforms other possible protocols, confirming the effectiveness of an in-network skyline as well as the join filtering capabilities of our protocol. [ABSTRACT FROM AUTHOR]

Since atomic or functional‐group properties in the bulk are generally not available from experimental methods, computational approaches based on partitioning schemes have emerged as a rapid yet accurate pathway to estimate the materials behavior from chemically meaningful building blocks. Among several applications, a comprehensive and systematically built database of atomic or group polarizabilities and related opto‐electronic quantities would be very useful not only to envisage linear or non‐linear optical properties of biomacromolecules but also to improve the accuracy of classical force fields devoted to simulate biochemical processes. In this work, we propose the first entries of such database that contains distributed polarizabilities and dipole moments extracted from fragments of peptides. Twenty three prototypical conformers of the dipeptides alanine–alanine and glycine–glycine were used to extract functional groups such as CH2, CHCH3, NH2, COOH, CONH, thus allowing construction of a diversity of chemically relevant environments. To evaluate the accuracy of our database, reconstructed properties of larger peptides containing up to six residues of alanine and glycine were tested against density functional theory calculations at the M06‐HF/aug‐cc‐pVDZ level of theory. The procedure is particularly accurate for the diagonal components of the polarizability tensor with errors up to 15%. In order to include solvent effects explicitly, the peptides were also surrounded by a box of water molecules whose distribution was optimized using the CHARMM force field. Solvent effects introduced by a classical dipole–dipole interaction model were compared to those obtained from polarizable‐continuum model calculations. [ABSTRACT FROM AUTHOR]

This article presents the architecture of integration of blockchain technology (BCT) and the Internet of Things with the planning of production processes. The authors proposed a shared concept of a distributed machine database based on BCT. As part of the work, a network of connections for the exchange of production resources was created using nodes communicating in a decentralized system, which at the same time serves as an integration of the virtual and real environment. Particular attention was focused on developing an algorithm for the efficient division of production tasks between all interested network users. BCT is used to conclude smart contracts and transactions and ensure the security of exchanged production data within shared ledgers. The proposed concept is a solution enabling a modern approach to the interdisciplinary management of production resources while maintaining the highest cybersecurity standards. [ABSTRACT FROM AUTHOR]

In the last few years, innovative technology and health care digitalization played a major role in all medical fields and a great effort worldwide to manage this large amount of data, in terms of security and digital privacy has been made by different national health systems. Blockchain technology, a peer-to-peer distributed database without centralized authority, initially applied to Bitcoin protocol, soon gained popularity, thanks to its distributed immutable nature in several non-medical fields. Therefore, the aim of the present review (PROSPERO N° CRD42022316661) is to establish a putative future role of blockchain and distribution ledger technology (DLT) in the organ transplantation field and its role to overcome inequalities. Preoperative assessment of the deceased donor, supranational crossover programs with the international waitlist databases, and reduction of black-market donations and counterfeit drugs are some of the possible applications of DLT, thanks to its distributed, efficient, secure, trackable, and immutable nature to reduce inequalities and discrimination. [ABSTRACT FROM AUTHOR]

Bitcoin is a popular decentralized cryptocurrency, and the Bitcoin network is essentially an unstructured peer-to-peer (P2P) network that can synchronize distributed database of replicated ledgers through message broadcasting. In the Bitcoin network, the average clustering coefficient of nodes is very high, resulting in low message propagation efficiency. In addition, average node degree in the Bitcoin network is also considerably large, causing high message redundancy when nodes use the gossip protocol to broadcast messages. These may affect message propagation speed, hindering Bitcoin from being applied to scenarios of high transactional throughputs. To illustrate, we have collected single-hop propagation data of transactions of 366 blocks from Bitcoin Core. The analysis results show that transaction verification and network delay are two major causes of low transaction propagation efficiency. In this paper, we propose a novel P2P network structure, called local clique network (LCN), for message broadcasting in the Bitcoin network. Specifically, to reduce transaction validation latency and message redundancy, in LCN local nodes (logically) form cliques, and only a few nodes in a clique broadcast messages to the other cliques, instead of each node sending messages to its neighboring nodes. We have conducted extensive experiments, and the results show that message redundancy is low in LCN, and message propagation speed increases significantly. Meanwhile, LCN exhibits excellent robustness when average node degree remains high in the Bitcoin network. [ABSTRACT FROM AUTHOR]

Over the last few decades, graphs have become increasingly important in many applications and domains for managing Big data. Big data analysis in a graph database is described as an analysis of exponentially increasing massive interconnected data concerning time. However, analyzing big connected data in social networks and synthetic identity detection is challenging. In previous approaches, fraud detection has been done on the complete graph data, which is a time-consuming process and will create bottlenecks while query execution. To overcome the issue, this paper proposes a new fraud detection technique to unveil synthetic identities involved in the Panama Paper leak dataset (unprecedented leak of 11.5 m data from the database of the world's fourth-biggest offshore law arm, Mossack Fonseca) using a Node rank-based fraud detection algorithm by integrating distributed data profiling techniques on a minimized graph by minimizing the least influential nodes. The proposed model is verified on the three nodes cluster to improve data scalability, reduce the query execution time by an average of 30–36% and finally reduce the fraud detection time by 18.2%. [ABSTRACT FROM AUTHOR]

FL can achieve secure distributed model training based on privacy protection technology without losing data ownership, but it also has some problems such as centralization and lack of fair incentives. Blockchain is a distributed database, which has the characteristics of decentralization, trust and notarization, but it also has some key problems, such as low network throughput and waste of resources. Aiming at the problems and characteristics of the above technical methods, this paper proposed a federated learning framework for data sharing driven by dual blockchain incentives, named FedSharing. It constructed the main chain and side chain respectively. The main chain used transactions to encapsulate the global parameters exchanged in federated learning, and combined on-chain smart contracts and off-chain capacity expansion technology to establish gradient state channels.Side chain proposed a new modified Shapley value proof-of-work algorithm (PoFS), which modified the traditional Shapley value calculation premise of equality of members, and took into account the factor affecting the interests of the alliance, the integrity of the cooperation history of members in federated learning. The test results show that the time of each round of the gradient state channel is reduced by 4～5 s on average compared with the smart contract decentralized scheme, and the incentive allocation ratio under the PoFS consensus is more in line with the fair reality. [ABSTRACT FROM AUTHOR]

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM $^{2}$ 2 , a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient. [ABSTRACT FROM AUTHOR]

We propose a novel unified frameork for automated distributed active learning (AutoDAL) to address multiple challenging problems in active learning such as limited labeled data, imbalanced datasets, automatic hyperparameter selection as well as scalability to big data. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes and jointly optimizing hyperparameters in both the classification and query selection stages. For dense datasets, clustering-based uncertainty sampling with maximum entropy (CME) loss is applied in the optimization. For sparse and imbalanced datasets, shrinkage optimized KL-divergence regularization and local selection based active learning (SOAR) loss are further naturally adapted in AutoDAL. The optimization is efficiently resolved by iteratively executing a genetic algorithm (GA) refined with a local generating set search (GSS) and solving an integer linear programming (ILP) problem. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and two real-world datasets including an electrocardiogram (ECG) dataset and a credit fraud detection dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms. [ABSTRACT FROM AUTHOR]

Mars' wet‐to‐dry transition is a major environmental catastrophe, yet the spatial pattern, tempo, and cause of drying are poorly constrained. We built a globally distributed database of constraints on Mars late‐stage paleolake size relative to catchment area (aridity index (AI)), and found evidence for climate zonation as Mars was drying out. Aridity increased over time in southern midlatitude highlands, where lakes became proportionally as small as in modern Nevada. Meanwhile, intermittently wetter climates persisted in equatorial and northern‐midlatitude lowlands. This is consistent with a change in Mars' greenhouse effect that left highlands too cold for liquid water except during a brief melt season, or alternatively with a fall in Mars' groundwater table. The data are consistent with a switch of unknown cause in the dependence of AI on elevation, from high‐and‐wet early on, to high‐and‐dry later. These results sharpen our view of Mars' climate as surface conditions became increasingly stressing for life. Plain Language Summary: Mars' surface was habitable in the past but is sterile today. Mars had multiple lake‐forming eras as the planet dried out, but so far, there has been no globally distributed survey of the size of late‐stage lakes, and the evaporation/precipitation ratio (aridity index (AI)) of the climates that formed them. This is key input/test data for models of Mars' past climate and climate evolution. We built a globally distributed database of AI constraints for late‐stage river‐forming climates on Mars. On average, late‐stage lake‐forming climates had a higher aridity than early‐stage river‐forming climates. Drying‐out was spatially heterogenous, with a "high‐and‐dry" pattern. This apparently contrasts with a "high‐and‐wet" pattern seen for early‐stage river‐forming climates. The reasons for this apparent switch are unknown. Key Points: Aridity on Mars increased over time, but intermittently wetter climates persisted in lowlandsConsistent with a change in Mars' greenhouse effect that left highlands too cold for liquid water except during a brief melt seasonData are consistent with switch, of unknown cause, in dependence of aridity index on elevation: high‐and‐wet early on, high‐and‐dry later [ABSTRACT FROM AUTHOR]

The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid the stragglers effect in synchronous FL and adapts to scenarios with vast participants. Both staleness and non-IID data in asynchronous FL would reduce the model utility. However, there exists an inherent contradiction between the solutions to the two problems. That is, mitigating the staleness requires to select less but consistent gradients while coping with non-IID data demands more comprehensive gradients. To address the dilemma, this paper proposes a two-stage weighted $K$ K asynchronous FL with adaptive learning rate (WKAFL). By selecting consistent gradients and adjusting learning rate adaptively, WKAFL utilizes stale gradients and mitigates the impact of non-IID data, which can achieve multifaceted enhancement in training speed, prediction accuracy and training stability. We also present the convergence analysis for WKAFL under the assumption of unbounded staleness to understand the impact of staleness and non-IID data. Experiments implemented on both benchmark and synthetic FL datasets show that WKAFL has better overall performance compared to existing algorithms. [ABSTRACT FROM AUTHOR]

We address the problem of federated learning (FL) where users are distributed and partitioned into clusters. This setup captures settings where different groups of users have their own objectives (learning tasks) but by aggregating their data with others in the same cluster (same learning task), they can leverage the strength in numbers in order to perform more efficient federated learning. For this new framework of clustered federated learning, we propose the Iterative Federated Clustering Algorithm (IFCA), which alternately estimates the cluster identities of the users and optimizes model parameters for the user clusters via gradient descent. We analyze the convergence rate of this algorithm first in a linear model with squared loss and then for generic strongly convex and smooth loss functions. We show that in both settings, with good initialization, IFCA is guaranteed to converge, and discuss the optimality of the statistical error rate. In particular, for the linear model with two clusters, we can guarantee that our algorithm converges as long as the initialization is slightly better than random. When the clustering structure is ambiguous, we propose to train the models by combining IFCA with the weight sharing technique in multi-task learning. In the experiments, we show that our algorithm can succeed even if we relax the requirements on initialization with random initialization and multiple restarts. We also present experimental results showing that our algorithm is efficient in non-convex problems such as neural networks. We demonstrate the benefits of IFCA over the baselines on several clustered FL benchmarks. [ABSTRACT FROM AUTHOR]

Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis. [ABSTRACT FROM AUTHOR]

We propose a novel unified frameork for automated distributed active learning (AutoDAL) to address multiple challenging problems in active learning such as limited labeled data, imbalanced datasets, automatic hyperparameter selection as well as scalability to big data. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes and jointly optimizing hyperparameters in both the classification and query selection stages. For dense datasets, clustering-based uncertainty sampling with maximum entropy (CME) loss is applied in the optimization. For sparse and imbalanced datasets, shrinkage optimized KL-divergence regularization and local selection based active learning (SOAR) loss are further naturally adapted in AutoDAL. The optimization is efficiently resolved by iteratively executing a genetic algorithm (GA) refined with a local generating set search (GSS) and solving an integer linear programming (ILP) problem. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and two real-world datasets including an electrocardiogram (ECG) dataset and a credit fraud detection dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms. [ABSTRACT FROM AUTHOR]