Smart contracts are essential tools for enabling interaction between blockchain and Internet of Things (IoT) systems. For example, in cold chain logistics, the blockchain can obtain the states of the logistics system through smart contracts. However, direct interactions between smart contracts and these systems introduce uncertainties, potentially leading to network forks or state inconsistencies, which can compromise the security and reliability of the blockchain. To address these challenges, a novel smart contract variable, ExState, is proposed, specifically designed to track and store the dynamic states of IoT systems. Additionally, a corresponding operational logic is defined to organize these states into sequential records, ensuring that the state sequences obtained by each node remain consistent, effectively mitigating state conflicts. In addition, a formal model is developed, accompanied by a theoretical analysis of its determinacy. Experimental results demonstrate that, in cross-chain scenarios, this method achieves a performance improvement of up to 50.41% compared to the traditional Oracle method.

Indexes are one of the important access structures that help improve database performance. This paper provides a methodology to automate the entire lifecycle of index creation and management with continuous index tuning based on changing data and workload. We present novel ideas that are critical to ensuring automatic indexing seamlessly works in a production database. Our methodology avoids using an expensive clone; yet offers non-intrusive index operations (candidate isolation and evaluation with Oracle resource manager ensuring no visible impact to the user workload), and upon deployment of auto indexes ensures non-disruptive plan invalidations and timely mitigation of performance regressions. The proposed approach is unique in that it is incremental and iterative, continually creating beneficial indexes and dropping unused ones as the workload evolves. The approach even supports indexes on expressions. It performs careful validation - including computing overhead of index maintenance incurred during DML while evaluating potential benefit - and provides accountability for its actions. Performance regressions are effectively managed using Oracle's powerful SQL Plan Management (SPM) framework. For example, a new automatic index isn't dropped in response to a single statement regressing due to it; SPM instead ensures such regressing statements revert to well-performing plans even in the presence of new indexes that continue to benefit other statements. We also share results of comprehensively evaluating various automatic indexing aspects in publicly available and Oracle customer workloads. Our experiments show benefit with automatic indexing, especially in customer workload, with a 15% improvement in performance and 60% space reclamation potential. This automatic indexing feature is available since Oracle 19c and in Oracle Autonomous Database.

In the rapidly evolving landscape of the sixth generation (6G) network, smart contracts emerge as a pivotal technology for enforcing trustful rules. However, the conventional lifecycle model of smart contracts—encompassing stages from initiation to the termination of a contract instance—suffers from rigidity and lack of customization, leading to notable operational challenges. These challenges primarily manifest as heightened resource demands, including longer waiting periods and escalated transaction costs, which hinder the adaptability of smart contracts in the varied and dynamic contexts of 6G-connected environments. Driven by these issues, this article conducts a comprehensive analysis of the smart contract lifecycle. We introduce an innovative lifecycle model that offers customizable flexibility, allowing for the merging or separation of different stages in the smart contract process. Meanwhile, we propose a unique transaction data structure designed to integrate parameters of combined stages, each marked with distinct identifiers for differentiation. Further, we introduce an innovative address scheme for smart contract instances, which provides an identifier to simplify instance access while also maintaining a mechanism for traditional access methods. The verification results show that the model can save 52.72% of processing fee and 68.09% of completion time compared with the conventional method.

In recent years, blockchain technology has been widely applied in the Internet of Things (IoT) field, where devices located in different blockchains need to interact with each other cross-chain. Existing cross-chain models have high-implementation complexity, long response times, and are difficult to apply in IoT scenarios. In this article, we propose keyword-based embedded smart contract cross-chain interoperability and collaboration model. This model identifies the status of smart contracts through the keywords of smart contracts, completes cross-chain operations, and achieves smart contract collaboration and asset exchange among devices in different blockchains. To find matching collaborative contracts, contract-specific keywords will be set, including asset amounts and types. In the cross-chain asset exchange scenario of the model, the assets sent by the sender are frozen in the contract. When the contract is successfully executed, the assets will be sent to the receiver, otherwise returned to the sender. In the cross-chain contract collaboration scenario, we identify the number of contract interactions, and when the contract collaboration is completed, the data will be retained, otherwise it will be invalidated. We also use an embedded smart contract method to further optimize IoT application scenarios. This method embeds the steps of deploying smart contracts into the invoked transaction, enabling the deployment and invocation of smart contracts through a single transaction. The experiments show that the keyword-based embedded contract method is 47% faster and 49% less costly compared to notary schemes solutions in cross-chain scenarios.

Query optimization must evolve with new workloads. As analytic and data warehouse workloads become more ubiquitous, optimization techniques that reduce the amount of data processed during query execution, enable shared computation and avoid expensive data access and joins must be rigorously explored. In this paper, we present aggregate-decomposition techniques as enhancements to an existing query transformation that performs grouping before joins. Consequently, the transformation generates more query rewrite candidates and can also be applied to a larger set of queries. Further, we introduce two new query transformations, i) subsumption of views and subqueries that explores opportunities for sharing computation and ii) union-all duplicator transformation for queries with disjunctive join predicates that removes the need for multiple data access and joins. These techniques are applicable to commonly noticed query patterns in customer workloads and provide significant performance benefit as indicated in our performance study. They have been implemented in Oracle RDBMS.

Mining causal relationships within co-location patterns is a crucial aspect of knowledge discovery, with broad applications spanning across ecosystems, praxiology, and epidemiology. However, existing approaches to solving this problem still have limitations. Some causality models assume rigid definitions of causation that may not generalize or do not fully leverage spatial-temporal information. Moreover, how to differentiate direct causes and indirect causes is also challenging. To address them, this paper proposes a generalized model for mining causality from co-location patterns. Our approach, rooted at Granger causality, integrates an algorithm that approximates direct causes from Granger causes by leveraging unique linear causal information. We conducted extensive experiments on real-world datasets to evaluate the effectiveness of our method and compared it with two baselines.

MOTIVATION: It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing. RESULTS: Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. AVAILABILITY AND IMPLEMENTATION: https://yanglab.nankai.edu.cn/UniDoc/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Internet of Things (IoT) devices and services have become increasingly ubiquitous in recent years as they greatly facilitate our daily life. To promote IoT data and semantic interoperability, we propose an edge–cloud framework. The edge end is responsible for handling customized data processing tasks and transferring the processed data, while the cloud end deals with semantic information processing. Additionally, we present an entity tree embedding algorithm at the cloud end to convert IoT entities and attributes into embedding vectors in a tree-structured way. Consequently, entity embeddings could reflect the semantic information at both Class and Property levels, which ameliorates our previous entity embedding method, leading to better embedding results and clustering effects. Finally, the entity tree embedding algorithm and the corresponding compression algorithm are evaluated. Results indicate that the Whitening algorithm is the best method to compress embedding vectors. More importantly, the entity tree embedding algorithm captures both the semantic and structural information of entities and attributes. Additionally, the clustering experiments show that the proposed embedding algorithm achieves better clustering results compared with the original entity embeddings and the uncompressed averaging method.

Blockchain exchanges have been widely used for IoT-based resource sharing. An exchange consists of several related transactions where participants exchange their assets. However, the exchange can take a long time to complete due to different reasons (such as IoT device failure). Meanwhile, exchange fees are an important aspect, because an exchange usually contains multiple transactions, and the exchange fee is the sum of the fees for each transaction. In this article, we explore ways to optimize the exchange process concerning the above issues. The method is based on the graph of an exchange, which is strongly connected. To save exchange time, we propose a way to split the exchange into smaller exchanges, since smaller exchanges have fewer transactions. The method first finds strongly connected subgraphs, separates them from the original graph, and adjusts the edges and their weights. To save transaction fees, we propose a method to reduce the number of transactions. Transactions that meet certain requirements are combined into one transaction, and corresponding weights are adjusted. The verification results show that the proposed method can optimize the exchange process, which can be used to save the exchange time and also can be used to save transaction fees.

Despite best coding practices, software bugs are inevitable in a large codebase. In traditional databases, when errors occur during query processing, they disrupt user workflow until workarounds are found and applied. Manual identification of workarounds often relies on a trial-and-error method. The process is not only time-consuming but also requires domain expertise that users are often lacking. In this paper, we propose a framework to automatically mitigate errors that occur during query compilation (including optimization and code generation) without any user intervention. An error is intercepted by the database internally, a workaround is identified for it, and the query is recompiled using the workaround. The entire process remains transparent to the user with the query being executed seamlessly. The proposed technique handles SQL errors during query compilation and provides three types of mitigation strategies - i) quickly failover to one of the readily-available historical plans for the statement ii) apply targeted error-correcting directives (hints) identified from the optimizer context at the time of the error iii) modify the global configuration of the optimizer using hints. This feature has been implemented and will be released in an upcoming version of Oracle Autonomous Database.

In the past few decades, the prevalence of Internet of Things (IoT) applications has brought both opportunities and challenges of different categories. One of the main concerns nowadays is semantic interoperability. Although plenty of augmented ontologies and resource description frameworks have been designed to achieve semantic interoperation, there is still no sustainable solution to interconnect the increasing number of heterogeneous devices and corresponding data. Aiming at this problem, this article presents a dataspaces model utilizing distributed approaches to represent semantic information as a sustainable solution for IoT semantic interoperability. In this model, an attention-based entity embedding approach is designed to convert IoT entities into low-dimensional dense vectors, then calculations, including entities, relations, etc., can be further conducted. Consequently, the entity embeddings could reflect the semantic information of Class equivalences among entities. To recognize entity relations, the generated entity vectors and the arithmetic results of two entities are combined as input features. By feeding the input features to a well-trained Tensor-train modified DNN, the relation between two entities could be recognized. Finally, experiments are conducted on two data sets to evaluate the proposed approaches. Results indicate that the generated entity vectors could effectively reflect the semantic similarity between entities. More importantly, while achieving parameter compression and better generalizing ability, the relation recognition approach improves the relation recognition accuracy on new entities compared with the state-of-art models.

In recent years, the blockchain technology has been widely used in the Internet of Things (IoT). One of the major concerns is how to adopt a smart contract to process data from IoT devices flexibly. While plenty of smart contract-based methods can be used, smart contracts are required to be deployed previously. This requires an additional step (to deploy a smart contract) and makes a smart contract separate from its data (transactions to trigger its interface), which generates limitations in IoT scenarios. In this article, we developed an approach to embed the smart contract and its data into the same transaction, eliminating the need for a predeployment step. Data is employed as parameters to invoke the interface of a smart contract, and the smart contract is used to process the data inside the transaction. With this method, a smart contract does not need other transactions from the user. Results indicate that the proposed method can eliminate the requirement of a separately deployed smart contract, saving costs, and waiting time for the predeployment.

Computation offloading has become popular in recent years as it is an effective way to reduce the energy consumption and enhance the performance of smartphones. To deal with the heterogeneous architectures between the smartphone and the server, and to simplify deployment of the server, we propose and implement a lightweight offloading framework which supports offloading of compute-intensive tasks and deploying the server efficiently. Based on this framework, generic and developer-customized offloading services could be provided for different third-party applications. Furthermore, we design a multi-task offloading tactic for the framework to deal with intensive offloading requests from various mobile devices. When receiving an offloading request, the master node in server-side determines whether this task should be offloaded or not and which VM should handle this task, so that the overall execution time and energy consumption are optimized. We implement this framework and evaluate it by comparing the execution time, energy consumption and CPU utilization rate among three execution modes with three applications. We also conduct experiments of the multi-task offloading tactic in simulation environment. Experimental results indicate that this framework effectively reduces energy consumption and boosts performance for compute-intensive tasks, and the multi-task offloading tactic is valid for intensive offloading requests.

MOTIVATION: RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites. RESULTS: We developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure's flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/RNAsite. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.

Smart contracts are required to be instantiated in the predeployed stage, which consumes computation resources from then on. It is a big waste in the blockchain whose nodes are composed of IoT devices, as those devices often have limited resources (such as limited power supplies or a limited number of processes to run). Meanwhile, IoT devices are heterogeneous and different smart contracts are required. If those smart contracts are instantiated previously, numerous meaningless addresses are required. In this paper, we propose to delay the instantiation of a smart contract when used and terminate it when not used, which is similar to the life cycle of a variable. Then, a new kind of variable (the wrapping variable) is used to hide details of the instantiation and the address. The smart contract is instantiated in the construction function of the wrapping variable, or even it is delayed to the time when there are requests for it. The smart contract terminates when the variable is out of its scope. Then, different instantiation methods are proposed. Finally, we perform the qualitative comparison between the proposed approach and the predeployment method, and it demonstrates that the proposed methods optimize the life cycle of the smart contract and save calculation resources.

MOTIVATION: The interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein-nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low. RESULTS: In this work, we introduce NucBind, a novel method for the prediction of nucleic acids-binding residues. NucBind combines the predictions from a support vector machine-based ab-initio method SVMnuc and a template-based method COACH-D. SVMnuc was trained with features from three complementary sequence profiles. COACH-D predicts the binding residues based on homologous templates identified from a nucleic acids-binding library. The proposed methods were assessed and compared with other peering methods on three benchmark datasets. Experimental results show that NucBind consistently outperforms other state-of-the-art methods. Though with higher accuracy, similar to many other ab-initio methods, cross prediction between DNA and RNA-binding residues was also observed in SVMnuc and NucBind. We attribute the success of NucBind to two folds. The first is the utilization of improved features extracted from three complementary sequence profiles in SVMnuc. The second is the combination of two complementary methods: the ab-initio method SVMnuc and the template-based method COACH-D. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/NucBind. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

New generation of analytic applications emerged to process data generated from non conventional sources. The challenge for the traditional database systems is that the data sets are very large and keep increasing at a very high rate while the application users have higher performance expectations. The most straightforward response to this challenge is to deploy larger hardware configurations making the solution very expensive and not acceptable for most cases. Alternative solutions fall into two categories: reduce the data set using sampling techniques or reduce the computational complexity of expensive database operations by using alternative algorithms. Alternative algorithms considered in this paper are approximate aggregates that perform a lot better at the cost of reduced and tolerable accuracy. In Oracle 12C we introduced approximate aggregates of expensive aggregate functions that are very common in analytic applications, that is, approximate count distinct and approximate percentile. The performance is improved in two ways. First, the approximate aggregates use bounded memory, often eliminating the need to use temporary storage which results in significant performance improvement over the exact aggregates. Second, we provide materialized view support that allows users to store pre-computed results of approximate aggregates. These results can be rolled up to answer queries on different dimensions (such rollup is not possible for exact aggregates).

Pitch is an important characteristic of speech and is useful for many applications. However, pitch determination in noisy conditions is difficult. In this paper, we propose a supervised learning algorithm to estimate pitch using a convolutional neural network (CNN). Specifically, we use a CNN for pitch candidate selection, and dynamic programming for pitch tracking. Our experimental results show that the proposed method can obtain accurate pitch estimation and they show good generalization ability to new speakers and noisy conditions. We credit the success to the use of CNN, which is suitable for modeling the shift-invariant spectral feature for pitch detection.

Large tables are often decomposed into smaller pieces called partitions in order to improve query performance and ease the data management. Query optimizers rely on both the statistics of the entire table and the statistics of the individual partitions to select a good execution plan for a SQL statement. In Oracle 10g, we scan the entire table twice, one pass for gathering the table level statistics and the other pass for gathering the partition level statistics. A consequence of this gathering method is that, when the data in some partitions change, not only do we need to scan the changed partitions to gather the partition level statistics, but also we have to scan the entire table again to gather the table level statistics. Oracle 11g adopts a one-pass distinct sampling based method which can accurately derive the table level statistics from the partition level statistics. When data change, Oracle only re-gathers the statistics for the changed partitions and then derives the table level statistics without touching the unchanged partitions. To the best of our knowledge, although the one-pass distinct sampling has been researched in academia for some years, Oracle is the first commercial database that implements the technique. We have performed extensive experiments on both benchmark data and real customer data. Our experiments illustrate the this new method is highly accurate and has significantly better performance than the old method used in Oracle 10g.

Execution plans for SQL statements have a significant impact on the overall performance of database systems. New optimizer statistics, configuration parameter changes, software upgrades and hardware resource utilization are among a multitude of factors that may cause the query optimizer to generate new plans. While most of these plan changes are beneficial or benign, a few rogue plans can potentially wreak havoc on system performance or availability, affecting critical and time-sensitive business application needs. The normally desirable ability of a query optimizer to adapt to system changes may sometimes cause it to pick a sub-optimal plan compromising the stability of the system. In this paper, we present the new SQL Plan Management feature in Oracle 11g. It provides a comprehensive solution for managing plan changes to provide stable and optimal performance for a set of SQL statements. Two of its most important goals are preventing sub-optimal plans from being executed while allowing new plans to be used if they are verifiably better than previous plans. This feature is tightly integrated with Oracle's query optimizer. SQL Plan Management is available to users via both command-line and graphical interfaces. We describe the feature and then, using an industrial-strength application suite, present experimental results that show that SQL Plan Management provides stable and optimal performance for SQL statements with no performance regressions.

This paper proposes a new technique of modeling driver drowsiness with multiple eyelid movement features based on an information fusion technique - partial least squares regression (PLSR), with which to cope with the problem of strong collinear relations among eyelid movement features and, thus, predicting the tendency of the drowsiness. With a set of electro- oculogram signals measured in an experiment conducted in Sweden, 14 typical eyelid movement features are first extracted. Then, statistical analyses from 20 subjects indicate that the eyelid movement parameters can characterize a driver's degree of drowsiness. The intrinsic quantitative relationships between eyelid movement features and driver drowsiness degree are modeled by PLSR analysis. The developed model provides a framework for integrating multiple sleepiness features together and defining the contribution of each feature to the decision and prediction result. The predictive precision and robustness of the model thus established are validated, which show that it provides a novel way of fusing multifeatures together for enhancing our capability of detecting and predicting the state of drowsiness.

XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data. While the automata model is naturally suited for pattern matching on tokenized XML streams, the algebraic model in contrast is a well-established technique for set-oriented processing of self-contained tuples. However, neither automata nor algebraic models are well-equipped to handle both computation paradigms.

No abstract available.

No abstract available.