SNG-TE: Sports News Generation with Text-Editing Model

Currently,the amount of sports news is increasing,given the number of sports available.As a result,manually writing sports news requires high labor costs to achieve the intended efficiency.Therefore,it is necessary to develop the automatic generation of sports news.Most available news gen-eration methods mainly rely on real-time commentary sentences,which have the following limitations:(1)unable to select suitable commentary sentences for news generation,and(2)the generated sports news could not accurately describe game events.Therefore,this study proposes a sports news generation with text-editing model(SNG-TE)is proposed to generate sports news,which includes selector and rewriter modules.Within the study context,a weight adjustment mechanism in the selector module is designed to improve the hit rate of important sentences.Furthermore,the text-editing model is introduced in the rewriter module to ensure that the generated news sentences can cor-rectly describe the game events.The annotation and generation experiments are designed to evaluate the developed model.The study results have shown that in the annotation experiment,the accuracy of the sentence annotated by the selector increased by about 8%compared with other methods.Moreover,in the generation experiment,the sports news generated by the rewriter achieved a 49.66 ROUGE-1 score and 21.47 ROUGE-2,both of which are better than the available models.Additionally,the proposed model saved about 15 times the consumption of time.Hence,the proposed model provides better performance in both accuracy and efficiency,which is very suitable for the automatic generation of sports news.

Mesh Generation for Finite Element Analysis of Electric Machines

This paper describes two modified methods for triangular and quadrilateral meshing for finite element analysis of 2D electric machines. One is coupling the classic Delaunay method and advancing front method to generate optimal triangulation; the other is coupling the classic paving and Delaunay triangulation for optimal quadrilateral meshing. Various electric machine models are meshed successfully to demonstrate the robustness and effectiveness of the methods.

Automatic Generation of the Planning Tunnel High Speed Craft Hull Form

The creation of geometric model of a ship to determine the characteristics of hydrostatic and hydrodynamic, and also for structural design and equipments arrangement are so important in the ship design process. Planning tunnel high speed craft is one of the crafts in which, achievement to their top speed is more important. These crafts with the use of tunnel have the aero-hydrodynamics properties to diminish the resistance, good sea-keeping behavior, reduce slamming and avoid porpoising. Because of the existence of the tunnel, the hull form generation of these crafts is more complex and difficult. In this paper, it has attempted to provide a method based on geometry creation guidelines and with an entry of the least control and hull form adjustment parameters, to generate automatically the hull form of plarming tunnel craft. At first, the equations of mathematical model are described and subsequent, three different models generated based on present method are compared and analyzed. Obviously, the generated model has more application in the early stages of design.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

Automatic Generation Method of Geological Cross-Sections in Dredging Engineering Based on 3D Solid NURBS Models

An automatic generation method of geological cross-sections in dredging engineering based on 3D geological solid models is presented.The 3D geological models are built applying the non-uniform rational B-splines(NURBS) technique,and a 2D profile can be calculated and generated automatically through Boolean operation to meet the demands of dredging projects.Moreover,an automatic marking method for geological attributes is put forward based on database technology,and the geological attributes include the profile name,scale,horizontal and vertical relative coordinates,geological lithology,and 2D standard lithology legend.At the same time,the automatic marking method can also provide an interactive mode for geological engineers to edit and modify the profile in the modeling system.Practical engineering applications show that the automatic generation method is a simple,flexible,fast and precise visual graphics rendering process that can create 2D standard profiles automatically and efficiently.This method also provides a convenient support tool for geological engineering digital analysis.

Reducing Item Exposure in Computerized Adaptive Testing Systems Using Automatic Item Generation

This paper studies the technics of reducing item exposure by utilizing automatic item generation methods. Known test item calibration method uses item parameter estimation with the statistical data, collected during examinees prior testing. Disadvantage of the mentioned item calibration method is the item exposure; when test items become familiar to the examinees. To reduce the item exposure, automatic item generation method is used, where item models are being constructed based on already calibrated test items without losing already estimated item parameters. A technic of item model extraction method from the already calibrated and therefore exposed test items described, which can be used by the test item development specialists to integrate automatic item generation principles with the existing testing applications.

AI-infused Semantic Model to Enrich and Expand Programming Question Generation

Creating practice questions for programming learning is not easy.It requires the instructor to diligently organize heterogeneous learning resources,that is,conceptual programming concepts and procedural programming rules.Today’s programming question generation(PQG)is still largely relying on the demanding creation task performed by the instructors without advanced technological support.In this work,we propose a semantic PQG model that aims to help the instructor generate new programming questions and expand the assessment items.The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network by the Local Knowledge Graph(LKG)and Abstract Syntax Tree(AST).For any given question,the model queries the established network to find related code examples and generates a set of questions by the associated LKG/AST semantic structures.We conduct analysis to compare instructor-made questions from 9 undergraduate introductory programming courses and textbook questions.The results show that the instructormade questions had much simpler complexity than the textbook ones.The disparity of topic distribution intrigued us to further research the breadth and depth of question quality and also to investigate the complexity of the questions in relation to the student performances.Finally,we report a user study results on the proposed Artificial Intelligent-infused semantic PQG model in examining the machine-generated questions’quality.

A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles

A hybrid Stochastic Fractal Search plus Pattern Search （hSFS-PS） based cascade PI-PD controller is suggested in this paper for Automatic Generation Control （AGC） of thermal, hydro and gas power unit based power systems in presence of Plug in Electric Vehicles （PEV）. Firstly, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral （I） controller is optimized by Stochastic FractaI Search （SFS） algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, differential evolution and teaching learning based optimization techniques is demonstrated by comparing simulation results for the identical power system. To improve the system performance further, Pattern Search （PS） is subsequently employed. The study is further extended for different controllers like PI, PID, and cascaded PI-PD controller and the superiority of cascade PI-PD controller over conventional controllers is demonstrated. Then, cascade PI- PD controller parameters of AGC searched using the proposed hSFS-PS algorithm in presence of plug in electric vehicles. The study is also extended to an interconnected power system. It is seen from the comparative analysis that hSFS-PS tuned PI-PD controller in single and multi-area with multi sources improves the system frequency stability in complicated situations. Lastly, a three area interconnected system with PEVs with dissimilar cascade PI-PD controller in each area is considered and proposed hSFS- PS algorithm is used to tune the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay.

Simulation-based automatic generation of risk scenarios

A methodology for automatically generating risk scenarios is presented.Its main idea is to let the system model ＂express itself＂ through simulation.This is achieved by having the simulation model driven by an elaborated simulation engine,which：（i） manipulates the generation of branch points,i.e.event occurrence times;（ii） employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point.In addition,a backtracking technique,as an extension,is implemented to recover some missed risk scenarios.A widely discussed dynamic reliability example（a holdup tank） is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.

Simulation and uniform design-based automatic generation of risk scenarios

This paper presents a methodology for automatically generating risk scenarios for dynamic reliability applications in which some dynamic characteristics(e.g.,the order,timing and magnitude of events,the value of relevant process parameters and initial conditions) have a significant influence on the evolution of the system.The main idea of the methodology is:(i) making the system model "express itself" through simulation by having the model driven by an elaborated simulation engine;(ii) exploiting uniform design to pick out a small subset of representative design points from the space of relevant dynamic characteristics;(iii) for each selected design point,employing a depth-first systematic exploration strategy to cover all possible scenario branches at each branch point.A highly dynamic example adapted from the literature(a chemical batch reactor) is studied to test the effectiveness of the proposed methodology.

Automatically Mining Application Signatures for Lightweight Deep Packet Inspection

Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.

Automatic generation of context-aware workflow documents for ubiquitous robot services

Intelligent robots in ubiquitous computing environment should be able to receive a variety of surrounding informa tion and provide users with appropriate services. A developer can describe the robot services that are proper to users＇ envir onments by using his or her various environments, and process them through the execution engine. However, it is difficult for a developer to describe and develop robot services, who knows all surrounding information which is called context infor mation. If there is a method for describing and documenting robot services in intuitive expressions, that is to use graphical user interfaces（GUls）, it would be very helpful. This paper suggests that robot service developers describe robot services us ing intuitive GUls with contextawareness. And the services can be automatically generated into workflow documents. Robot services that robot service developers have made with intuitive GUIs can be automatically generated into workflow docu ments by using the object modeling technique（OMT）. Developers can describe robot services based on contextaware work flow language（CAWL ）. For testing, scenariobased robot services are described using CAWLbased development tool, and their workflow documents are automatically generated.

Automatic Generation of UML Class Diagrams for Object-oriented Design and Programing Course

UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.

Automatic Generation of Amharic Math Word Problem and Equation

Math word problem uses a real word story to present basic arithmetic operations using textual narration. It is used to develop student’s comprehension skill in conjunction with the ability to generate a solution that agrees with the story given in the problem. To master math word problem solving, students need to be given fresh and enormous amount of problems, which normal textbooks as well as teachers fail to provide most of the time. To fill the gap, a few research works have been proposed on techniques to automatically generate math word problems and equations mainly for English speaking community. Amharic is a Semitic language spoken by more than hundred million Ethiopians and is a language of instruction in elementary schools in Ethiopia. And yet it belongs to one of a less resourced language in the field of linguistics and natural language processing (NLP). Hence, in this paper, a strategy for automatic generation of Amharic Math Word (AMW) problem and equation is proposed, which is a first attempt to introduce the use template based shallow NLP approach to generate math word problem for Amharic language as a step towards enabling comprehension and learning problem solving in mathematics for primary school students. The proposed novel technique accepts a sample AMW problem as user input to form a template. A template provides AMW problem with placeholders, type of problem and equation template. It is used as a pattern to generate semantically equivalent AMW problems with their equations. To validate the reality of the proposed approach, a prototype was developed and used as a testing platform. Experimental results have shown 93.84% overall efficiency on the core task of forming templates from a given corpus containing AMW problems collected from elementary school mathematics textbooks and other school worksheets. Human judges have also found generated AMW problem and equation as solvable as the textbook problems.

Automatic Generation Control in a Distributed Power Grid Based on Multi-step Reinforcement Learning

The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.

Knowledge Graph-Driven Weather Overview Generation for the Beijing 2022 Winter Olympic Games

Weather Overview is regarded as one of the crucial meteorological services supporting the Beijing 2022 Olympic and Paralympic Winter Games(hereafter as Beijing 2022).As generation of Weather Overview involves multipledata,large-scale weather conditions,and vulnerability to weather changes,there still exist quite some challenges in obtaining Weather Overview.At present,knowledge graph(KG)is believed to be an effective way to describe information and knowledge.Thus,this study focuses on development of a framework to automatically generate Weather Overview using KG.We first present a three-layer KG model to generate accurate content of Weather Overview:(1)knowledge acquisition of entities and relationships to construct the specific corpora;(2)knowledge representation of the relationships between weather conditions and the events based on ontology;and(3)knowledge application of corpora,variables,and weather conditions to query and reason knowledge with Neo4j.Moreover,an XML Schema is used to achieve the standardized Weather Overview,which is formed by sentence-paragraph-text generation.This model is validated for a typical case at the Yanqing National Alpine Skiing Centre in Beijing 2022.Compared to the manual method,the accuracy and standardization of Weather Overview can be maintained above 90%,and it can be automatically generated within seconds.The method proposed in this study provides a helpful meteorological service solution to other large-scale sports events.

Automatic Generation Control with Virtual Synchronous Renewables

As synchronous generators(SGs)are gradually displaced by renewable energy sources(RESs),the frequency stability of power systems deteriorates because RESs,represented by utility-scale solar and wind power sources,do not provide the inertial response,primary frequency response,secondary frequency response,and tertiary frequency regulation.As a result,the remaining SGs may not be sufficient to maintain the power balance and frequency stability.The concept and control strategies of virtual synchronous generators(VSGs)enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems.This paper focuses on the automatic generation control(AGC)with virtual synchronous renewables(VSRs).First,the VSR strategy that enables the RESs to participate in AGC is introduced.Second,based on the interval representation of uncertainty,the output of RES is transformed into two portions,i.e.,the dispatchable portion and the stochastic portion.In the dispatchable portion,the RESs can participate in AGC jointly with SGs.Accordingly,a security-constrained economic dispatch(SCED)model is built considering the RESs operating in VSR mode.Third,the solution strategy that employs the slack variables to acquire deterministic constraints is introduced.Finally,the proposed SCED model is solved based on the 6-bus and 39-bus systems.The results show that,compared with the maximum power point tracking(MPPT)mode,VSRs can participate in the active power and frequency control jointly with SGs,increase the maximum penetration level of RESs,and decrease the operating cost.

Research of testing method based on UML statecharts

Unified modeling language （UML） is a powerful graphical modeling language with intuitional meaning. It provides various diagrams to depict system characteristics and complex environment from different viewpoints and different application layers. UML-based software development and modeling environments have been widely accepted in industry, including areas in which safety is an important issue such as spaceflight, defense, automobile, etc. To ensure and improve software quality becomes a main concern in the field. As one of the key techniques for software quality, software testing can effectively detect system faults. UML based software testing based is an important research direction in software engineering. The key to software testing is the generation of test cases. This dissertation studies an approach to generating test cases from UML statecharts.

A virtual logic analyzer implemented with Arduino

A simple,stable and reliable virtual logic analyzer is presented. The logic analyzer had two modules:one was the test pattern generation module,the other was the logic monitoring module. Combining the two modules,one is able to test a digital circuit automatically. The user interface of the logic analyzer was programmed with LabVIEW. Two Arduino UNO boards were used as the hardware targets to input and output the logic signals. The maximum pattern update rate was set to be 20 Hz. The maximum logic sampling rate was set to be 200 Hz. After twelve thousand cycles of exhaustive tests,the logic analyzer had a 100% accuracy. As a tutorial showing how to build virtual instruments with Arduino,the software detail is also explained in this article.

Automatic Migration of a Software Task to Hardware Component in MPSoC Systems

The authors will focus on the study of the design of Multiprocessor Systems on Chip （MPSoC）, specifically in the context of improving the performance of applications located on the MPSoC architecture. The objective of this research is to study the problems of transition from a pure software implementation for an embodiment admitting one or more hardware components and to develop a methodology for automatic generation of migration of a software task into a hardware component in MPSoC. The transformation of a software task into a hardware task led to many changes, hardware part （connection, the requirement of an interrupt controller...）, software part （at least one task, I/O （I/O）, synchronization...） and an architectural point of view, the remarkable aspects of data storage. The experiment is done on the MJPEG decoder to illustrate the effectiveness of the authors＇ tool for automatic generation of migration.