Search

In this communication we aim to provide an overview of Earth observation and remote sensing in archaeology following a non-traditional literature review approach, that is, investigating recent developments in artificial intelligence (AI) and language models. Towards this direction, the generative pre-trained transformer (ChatGPT) language model was used to extract relevant information. The ChatGPT language model—recently released by OpenAI—appears to provide an alternative way for retrieving comprehensive information for various thematic topics. ChatGPT is currently operated on a beta version by millions of users worldwide, free of access for a limited period. In this study, specific queries related to Earth observation and remote sensing in archaeology were made by the authors to the ChatGPT. Innovations and limitations are discussed, while a comparison with traditional bibliographic analysis is performed.

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry.

The R&D (Research & Development) process of mobile application can be regarded as a complex system engineering that needs to take into account a variety of influencing elements, such as customer’s demands, engineering measures and business goals etc., as well as their interplay. A novel decision support platform is proposed to assist project managers in determining the optimal technical measures with implementation degrees and resource allocation scheme. Specially, the HDQ (House of Development Quality) is designed to collect essential data and information as well as reflect correlations among elements, which can effectively connect isolated information islands. Through the transform of HDQ, a multi-objective programming model is constructed to meet customers’ overall demands and specific requirements. A combination of specified technical measures and resource allocation can be obtained under ideal conditions. Additionally, the extended model considering resource substitution is designed to realize further improvement by effectively utilizing the surplus resource in the above optimal condition, demonstrating the advantage of proper managerial arrangement and resource deployment. Finally, the R&D case of family financial management application is conducted to illustrate the effectiveness of the suggested method, which offers a new perspective on mobile application R&D project management.

The field of edge computing has grown considerably over the past few years, with applications in artificial intelligence and big data processing, particularly due to its powerful accelerators offering a large amount of hardware parallelism. As the computing power of the latest edge systems increases, applications of edge computing are being expanded to areas that have traditionally required substantially high-performant computing resources such as scientific computing. In this paper, we review the latest literature and present the current status of research for implementing high-performance computing (HPC) on edge devices equipped with parallel accelerators, focusing on software environments including programming models and benchmark methods. We also examine the applicability of existing approaches and discuss possible improvements necessary towards realizing HPC on modern edge systems.

In recent years, the edge computing paradigm has been attracting much attention in the Internet-of-Things domain. It aims to push the frontier of computing applications, data, and services away from the usually centralized cloud servers to the boundary of the network. The benefits of this paradigm shift include better reactivity and reliability, reduced data transfer costs toward the centralized cloud servers, and enhanced confidentiality. The design of energy-efficient edge compute nodes requires, among others, low power cores such as microprocessors. Heterogeneous architectures are key solutions to address the crucial energy-efficiency demand in modern systems. They combine various processors providing attractive power and performance trade-offs. Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing. Various heterogeneous multicore designs are developed and prototyped on FPGA for unbiased evaluation. These designs rely on cost-effective and inherently ultra-low power cores commercialized by Cortus SA, a world-leading semiconductor IP company in the embedded ultra-low power microcontroller domain. Some microarchitecture-level design considerations, e.g., floating point and out-of-order computing capabilities, are taken into account for exploring candidate solutions. In addition, a tailored and flexible multi-task programming model is defined for the proposed architecture paradigm. We analyze the behavior of various application programs on available core configurations. This provides valuable insights on the best architecture setups that match program characteristics, so as to enable increased energy-efficiency. Our experiments on multi-benchmark programs show that on average 22% energy gain can be achieved (up to 45%) compared to a reference system design, i.e., a system with the same execution architecture, but agnostic of the task management insights gained from the comprehensive evaluation carried out in this work.

The Map-Join-Reduce programming model effectively supports the join operation among different heterogeneous data sets by adding the Join module and processes the multiway joining directly. In this paper, we propose a rigorous description of the Map-Join-Reduce that performs as an executable specification. First, this paper briefly introduces the differences between the Map-Join-Reduce and the MapReduce. Then, we use Haskell to specify each module of the Map-Join-Reduce programming model and analyze the structure and function of each module. Finally, we test the specification by analyzing an example of the mall sales records. The executable specification contributes to helping the developers to unscramble the relationship between the MapReduce and the Map-Join-Reduce, which may serve as a basis for further development of the theory of related programming model design. Furthermore, the most important function of an executable specification is guaranteeing the target informal or semi-formal model with interesting properties. This paper is a forward step to prepare for verifying related properties and, even, providing verified prototypes.

Lattice QCD is a frontier scientific field for studying elementary particles by numerical simulation methods, which has become one of the major scientific research applications of supercomputers.With the rapid development of Chinese supercomputers, the LQCD softwares need to be refactored due to the limitation of its traditional programming model.The characteristics of scientific applications on super computers from the perspective of lattice QCD were reviewed.A novel programming model targeted to Chinese super computers was proposed to adapt large-scale scientific applications with big data processing, which is a promising development direction for the basic softwares of Chinese supercomputing ecosystem.

The subject of this paper is a program verification method that takes into account abortion caused by partial functions in program statements. In particular, boolean expressions of various statements will be investigated that are not well-defined. For example, a loop aborts if its execution begins in a state for which the loop condition is undefined. This work considers the program constructs of nondeterministic sequential programs and also deals with the synchronization statement of parallel programs introduced by Owicki and Gries [7]. The syntax of program constructs will be reviewed and their semantics will be formally defined in such a way that they suit the relational model of programming developed at Eőtvős Loránd University [3, 4]. This relational model defines the program as a set of its possible executions and also provides definition for other important programming notions like problem and solution. The proof rules of total correctness [2, 5, 8, 9, 7] will be extended by treating abortion caused by partial functions. The use of these rules will be demonstrated by means of a verification case study.

Consistency analysis is a crucial topic for preference relations. This paper studies the consistency of interval linguistic fuzzy preference relations (ILFPRs) using the constrained interval linguistic arithmetic and introduces a new consistency definition. Then, several properties of this definition are researched. Meanwhile, the connection between this concept and a previous one is discussed. Following this concept, programming models for judging the consistency and for deriving consistent ILFPRs are constructed, respectively. Considering the case that incomplete ILFPRs may be obtained, a programming model for obtaining missing judgments following the consistency discussion is built. Afterwards, the consensus for group decision making (GDM) is studied and a model for adjusting individual ILFPRs to reach the consensus threshold is established. Consequently, an interactive procedure for GDM with ILFPRs is presented. A practical problem is provided to illustrate the utilization of the new algorithm and comparative discussion is offered.

With the increasing complexities, such as massive computing devices and strict requirements for collaboration, of industrial production systems, the concept of the cyber-physical production system (CPPS) is considered as a promising approach for addressing these challenges. However, programmability is a challenge in CPPSs. The traditional chimney style of programming requires considerable design effort from engineers to handle the spatiotemporal heterogeneity of cyber-physical model that encompasses computing, temporal, and geometric semantics along with physical dynamics. Thus, an easy-to-use programming model and an integrated programming framework that are suited to this context are required. However, the existing programming models typically fully consider only computing while only partially considering temporal characteristics, and they rarely consider geometric semantics. To solve this problem, this paper proposes a novel event-based programming model, GePro, considering the geometric spatial semantics to realize integrated programming and reduce design effort, especially the reconfiguration of adding new physical devices into the exist system. A prototype of GePro is implemented and verified based on IEC 61499 by the implementation of design and reconfigure assembly CPPS. The results show that using GePro results in a programming time savings of an development life cycle time compared to traditional models for reconfiguration.

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.

The application of big data and artificial intelligence is promoted by data mining and intelligent analysis of a large number of static data using big data computing platform.In the face of the growing demand for real-time dynamic data processing generated by the Internet of things,dataflow computing has been gradually introduced into some big data processing platforms.Aiming at the programming model of data flow,the traditional software engineering design method for dataflow analysis and the structure definition and model reference provided by the current dataflow programming model for big data processing platform was compared,the differences and shortcomings were analyzed,and the main features and key elements of the dataflow programming model were summarized.The main methods of dataflow programming and the combination with the mainstream programming tools were analyzed,and the basic framework and programming mode of visual dataflow programming tools were presented according to the dataflow computing business requirements of big data processing.

Recent advances in Smart City infrastructures and the Internet of Things represent a significant opportunity to improve people’s quality of life. Corresponding research often focuses on Cloud-centric network architectures where sensor devices transfer collected data to the Cloud for processing. However, the formidable traffic generated by countless IoT devices and the need for low-latency services raise the need to move away from centralized architectures and bring the computation closer to the data sources. To this end, this paper discusses SPF, a middleware solution that supports IoT application development, deployment, and management. SPF runs IoT services on capable devices located at the network edge and proposes an information-centric programming model that takes advantage of decentralized computation resources located in the proximity of application users and data sources. SPF also adopts Value-of-Information based methods to prioritize the transmission of essential information.

In accordance with the new characteristics of physical,internet and intelligence of CPS,a hybrid scheduling system was designed for CPS node operating system with real-time performance,flexibility,and adaptability.Based on the in-depth study of the intrinsic differences between event-driven scheduling mechanism and multithreaded one,a hybrid programming model was designed that combines collaborative multithread and hybrid stack management using adapter mechanism.A hybrid scheduling model was also designed that supports both event and thread task types.Reasonable real-time algorithms were implemented for verification.Experimental results show that the real-time per-formance of the scheduling system is obviously better than that of TinyOS,thus it is better suited for CPS than TinyOS does.

In accordance with the new characteristics of physical,internet and intelligence of CPS,a hybrid scheduling system was designed for CPS node operating system with real-time performance,flexibility,and adaptability.Based on the in-depth study of the intrinsic differences between event-driven scheduling mechanism and multithreaded one,a hybrid programming model was designed that combines collaborative multithread and hybrid stack management using adapter mechanism.A hybrid scheduling model was also designed that supports both event and thread task types.Reasonable real-time algorithms were implemented for verification.Experimental results show that the real-time per-formance of the scheduling system is obviously better than that of TinyOS,thus it is better suited for CPS than TinyOS does.