The combination of Sentinel-2 multispectral instrument (MSI) and Landsat 8 operational land imager (OLI) creates a virtual constellation of decametric sensors with high revisiting frequency. However, the differences in the spectral characteristics of the two sensors cause inconsistencies in downstream applications. This study proposed a multiband constraint spectral harmonization method called HARMU. In comparison to existing methods, HARMU uses all the spectral bands in the source sensor to predict the reflectance of the targeting sensor and so fully exploits spectral linkage among different bands. HARMU was specifically implemented by Gaussian process regression (GPR), with training data collected from the spatiotemporally representative BEnchmark Land Multisite ANalysis and Intercomparison of Products 2.1 (BELMANIP2.1) sites. We reproduced the top of the canopy reflectance at both common bands of OLI and MSI and also reflectance at red-edge (RE) bands that are only equipped on MSI. The results indicated that HARMU performed satisfactorily with$R^{2}$larger than 0.91 and Rel-Bias less than 0.19 for all bands over BELMANIP2.1 sites. HARMU offered similar performances as the widely used Harmonized Landsat and Sentinel-2 (HLS) products: average$R^{2}$slightly improved from 0.86 for HLS to 0.88 for HARMU for the common bands as evaluated over ground-based observations for validation (GBOV) sites, and additionally, it well reconstructs the missing RE band in HLS-based OLI ($R^{2} \gt 0.81$and Rel-Bias <0.15). HARMU will substantially contribute to generating spatiotemporally continuous time series of decametric data from the MSI-OLI virtual constellation and monitoring vegetation dynamics in large-scale and long-time sequences.

Network clustering (graph clustering) plays a crucial role in discovering the inherent structures within networks. MapReduce-based structural clustering algorithm for networks (MRSCANs) designed based on MapReduce's parallel computing model, efficiently handles large-scale data. However, MRSCAN can only be tested through experiments, and its correctness cannot be guaranteed. To address this issue, this article has achieved the first implementation of functional MRSCAN modeling and subjected it to rigorous mechanized verification in Isabelle. First, based on Google's MapReduce model type definition and higher-order generic functions, a general MapReduce-based algorithm functional modeling framework is constructed across the fundamental global phases of Map, Shuffle, and Reduce. Moreover, diverse strategies are devised during the Shuffle phases according to user requirements, enhancing the applicability and generality of the MapReduce functional modeling framework. Second, formalizing the definition of MRSCAN, is delineated into four key steps: similarity calculation, core calculation, dimension expansion, and structural clustering. Furthermore, the MapReduce functional modeling framework is applied to these four steps to achieve the functional modeling of MRSCAN, which improves efficiency compared to other structural clustering algorithm for networks (SCANs) algorithms. Lastly, a verification framework for MapReduce-based algorithms is proposed at both the global and shuffle stages. Based on this framework, the correctness and reliability of MRSCAN are ensured. The model framework and verification framework of MapReduce-based algorithms proposed in this article can not only address functional modeling and verification of MRSCAN but also provide a reference for a series of other MapReduce-based functional program designs and proofs.

When cyber-physical systems (CPSs) are operational, its computing units frequently interact with complex and uncertain physical environments in time and space. To ensure the safety of the system, it is often necessary that the physical entities and information systems of CPS operate in a consistent manner at the temporal and spatial levels. However, most of the existing studies on spatio-temporal consistency modeling and verification of CPS are limited in the ability to deal with uncertainties. To address this issues, in this article, we propose a modeling and verification method for spatio-temporal consistency of CPS in uncertain environments. First, we propose a modeling language (stochastic spatio-temporal modeling language, SSTL) for the spatio-temporal domain of CPS. It can explicitly model the spatio-temporal constraints of CPS as well as deal with the spatio-temporal behavior of accompanying probabilities. Second, we propose a framework for spatio-temporal consistency verification. In the first step of this framework, we propose a worst-case time satisfiability algorithm to verifying the time safety of CPS. In the second step, we develop a prototype tool called “SSTL2NSHA” that is able to convert SSTL into the NHSA model supported by UPPAAL-statistical model checking (UPPAAL-SMC). Thereby the CPS model described by SSTL can be verified in UPPAAL-SMC for spatial safety constraints. Finally, we illustrate the effectiveness of the approach in this article with a traffic alert and collision avoidance system.

API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge graph (KG)-based, but limitations include out-of-vocabulary (OOV) failures and rigid question templates. To address these limitations, we propose a novel knowledge-guided query clarification approach for API recommendation that leverages a large language model (LLM) guided by KG. We utilize the LLM as a neural knowledge base to overcome OOV failures, generating fluent and appropriate clarification questions and options. We also leverage the structured API knowledge and entity relationships stored in the KG to filter out noise, and transfer the optimal clarification path from KG to the LLM, increasing the efficiency of the clarification process. Our approach is designed as an AI chain that consists of five steps, each handled by a separate LLM call, to improve accuracy, efficiency, and fluency for query clarification in API recommendation. We verify the usefulness of each unit in our AI chain, which all received high scores close to a perfect 5. When compared to the baselines, our approach shows a significant improvement in MRR, with a maximum increase of 63.9% higher when the query statement is covered in KG and 37.2% when it is not. Ablation experiments reveal that the guidance of knowledge in the KG and the knowledge-guided pathfinding strategy are crucial for our approach's performance, resulting in a 19.0% and 22.2% increase in MAP, respectively. Our approach demonstrates a way to bridge the gap between KG and LLM, effectively compensating for the strengths and weaknesses of both.

Programmers who work with smart contract development often encounter challenges in reusing code from repositories. This is due to the presence of two unknowns that can lead to non-functional and functional failures. These unknowns are implicit collaborations between functions and subtle differences among similar functions. Current code mining methods can extract syntax and semantic knowledge (known knowledge), but they cannot uncover these unknowns due to a significant gap between the known and the unknown. To address this issue, we formulate knowledge acquisition as a knowledge deduction task and propose an analytic flow that uses the function clone as a bridge to gradually deduce the known knowledge into the problem-solving knowledge that can reveal the unknowns. This flow comprises five methods: clone detection, co-occurrence probability calculation, function usage frequency accumulation, description propagation, and control flow graph annotation. This provides a systematic and coherent approach to knowledge deduction. We then structure all of the knowledge into a semantic-enriched code Knowledge Graph (KG) and integrate this KG into two software engineering tasks: code recommendation and crowd-scaled coding practice checking. As a proof of concept, we apply our approach to 5,140 smart contract files available on Etherscan.io and confirm high accuracy of our KG construction steps. In our experiments, our code KG effectively improved code recommendation accuracy by 6% to 45%, increased diversity by 61% to 102%, and enhanced NDCG by 1% to 21%. Furthermore, compared to traditional analysis tools and the debugging-with-the-crowd method, our KG improved time efficiency by 30 to 380 seconds, vulnerability determination accuracy by 20% to 33%, and vulnerability fixing accuracy by 24% to 40% for novice developers who identified and fixed vulnerable smart contract functions.

Software programming requires both API reference (know-what) knowledge and programming task (know-how) knowledge. Lots of programming know-what and know-how knowledge is documented in text, for example, API reference documentation and programming tutorials. To improve knowledge accessibility and usage, several recent studies use Natural Language Processing (NLP) methods to construct API know-what knowledge graph (API-KG) and programming task know-how knowledge graph (Task-KG) from software documentation. Although being promising, current API-KG and Task-KG are independent of each other, and thus are void of inherent connections between the two types of knowledge. Our empirical study on Stack Overflow questions confirms that only 36% of the API usage problems can be answered by the know-how or the know-what knowledge alone, while the rest questions requires a fusion of both. Inspired by this observation, we make the first attempt to fuse API-KG and Task-KG by API entity linking. This fusion creates nine categories of API semantic relations and two types of task semantic relations which are not present in the stand-alone API-KG or Task-KG. According to the definitions of these new API and task semantic relations, our approach dives deeper than surface-level API linking of API-KG and Task-KG, and infer nine categories of API semantic relations from task descriptions and two types of task semantic relations with the assistance of API-KG, which enrich the declaration or syntactic relations in the current API-KG and Task-KG. Our fused and semantically-enriched API-Task KG supports coherent API/Task-centric knowledge search by text or code queries. We have implemented our approach on Java programming documentation and built a web tool to search and explore API and programming task knowledge. Our evaluation confirms the high-accuracy of our knowledge extraction, fusion and enrichment methods, and the effectiveness and usefulness of our API-Task KG for answering Stack Overflow questions.

Spatiotemporally continuous monitoring of aboveground biomass (AGB), an important indicator of grassland productivity, is crucial for achieving sustainable grassland development. Most existing grassland AGB estimation methods are empirical, and their temporally and spatially specific nature hinders operational application at large scales. Grass is herbaceous, so its AGB can be represented as the product of leaf area index (LAI) and dry matter content ($C_{m}$), both are the inputs of PROSAIL model. We, therefore, proposed a novel physical-based method through PROSAIL model inversion. Results showed that the estimated AGB presented good consistency with field-measured one, with$R^{2}= 0.87$and RMSE = 14.29 g/m2. We then implemented our method on the Google Earth Engine platform and generated daily and monthly AGB products covering the Tibetan Plateau (TP) and spanning from 2000 to 2021. These products characterized the spatiotemporally continuous dynamics of AGB on the TP. For example, it captured the decrease in dry matter caused by grazing during grassland dormancy, which is impossible for other existing AGB retrieval methods. Our method provides a promising tool to generate spatiotemporally continuous grassland AGB, which would inform the decision making for the conservation and restoration of grassland.

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9–60.9[Formula: see text] in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4–11[Formula: see text] in Precision.

The tree structure algorithms have been widely used in many computer fields. Developing efficient and reliable tree structure algorithms is a challenging problem in the field of software formalization and trusted software. In this paper, initially, the binary tree algorithms are divided into three kinds through induction of the loop invariant structures and output features. Then, PAR method can conveniently develop loop invariants and corresponding non-recursive algorithm programs. Finally, Isabelle is used to formally verify these developed algorithms. This development method not only overcomes the tediousness and error-proneness of traditional manual verification, but also greatly improves the efficiency and reliability of the developed algorithm program. To the best of our knowledge, this is the maiden attempt in the literature to verify a series of non-recursive and efficient binary tree algorithms. The above process forms a theorem proving library that include data types, data structures and lemma related binary tree algorithms, which can significantly reduce the cost of future verification.

Land surface albedo analysis and prediction are of great significance for global energy budget research and global change forecasting. Research has been performed on time series albedo analysis but seldom attempt was performed on land surface albedo prediction. This article develops an effective method for land surface albedo prediction from Moderate-Resolution Imaging Spectroradiometer (MODIS) time series albedo data (MCD43A3). It consists of time series data decomposing and time series data forecasting. The ensemble empirical mode decomposition (EEMD) method decomposes the MODIS historical time series albedo data into several intrinsic mode functions (IMFs) and one residual series, then the nonlinear autoregressive neural network (NARnet) method is used to forecast each IMF component and residue. The predictions of all IMFs and residue are summed to obtain a final forecast for the albedo series. The proposed method was performed on monthly and daily albedo prediction both in snow-free and snowy areas. The results showed that the forecast albedo consists of the MODIS albedo data well, with R2greater than 0.89 and RMSE less than 0.052 for snow-free areas. For snowy areas, the forecasting also performed well during snow cover periods, with R2greater than 0.76 and RMSE less than 0.076. For irregular change periods of snow falling and melting, it is hard to get very high prediction accuracy due to the irregular land surface change. For this problem, more land surface information should be introduced, or adjusting the model over time is necessary.

The core of object-oriented programming (OOP) is the object, while the core of generic programming (GP) is the type requirement. Generic programming can improve the reusability, security and development efficiency of programs. Generic constraints can prevent run-time crashes of generic programs. Generics constraints and their matching mechanism in the mainstream programming languages are studied. The abstract degree of the current mainstream languages is not high enough to describe the complex dynamic semantic requirements. Abstract generic programming language Apla is used as the host language to propose a generic constraint method based on the type requirements of complete GP, including static syntax generic constraint and dynamic semantic generic constraint. A general generic constraint description language is given, a matching detection algorithm is designed to determine whether the static syntax generic constraints meet the requirements, and a matching verification mechanism is designed for dynamic semantic generic constraint verification. Finally, the whole process of matching detection and verification is demonstrated by taking Kleene algorithm as an example. The generic constraint mechanism can improve the security of the program. It further implements the GP concept with the type requirement as the core in the full sense.

Leaf area index (LAI) is an important parameter for describing vegetation change and growth trend. Due to various problems of satellite observation and retrieval algorithm, spatiotemporal complete LAI data is limited, which hindered the application of LAI in different areas. In this paper, a data fusion method, multiresolution tree (MRT) model, was used to develop LAI of different spatial resolution. Three LAI data sets of Landsat (30 m), MODIS (450 m) and GLASS (900 m) are used. MRT was performed in Ukraine area including farmland, forestland and grassland. Results indicate that MRT is an efficient algorithm to get spatial complete LAI estimation at different resolution. LAI of different spatial resolution consists well, R2and RMSE are 0.8612 and 0.3986 when compare Landsat LAI with MODIS LAI, which are 0.7568 and 0.4736 when compare Landsat with Glass.