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.

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.

Designing and Optimization of an Off-line Programming System for Robotic Belt Grinding Process

Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the OLP system. A new type of grinding robot and a novel robotic belt grinding workcell are forwarded, and their features are briefly introduced. An open and object-oriented off-line programming system is developed for this robotic belt grinding system. The parameters of the trimmed surface are read from the initial graphics exchange specification （IGES） file of the CAD model of the workpiece. The deBoor-Cox basis function is used to sample the grinding target with local contact frame on the workpiece. The numerical formula of inverse kinematics is set up based on Newton＇s iterative procedure, to calculate the grinding robot configurations corresponding to the grinding targets. After the grinding path is obtained, the OLP system turns to be more effective than the teach-by-showing system. In order to improve the grinding workspace, an optimization algorithm for dynamic tool frame is proposed and performed on the special robotic belt grinding system. The initial tool frame and the interval of neighboring tool frames are defined as the preparation of the algorithm. An optimized tool local frame can be selected to grind the complex surface for a maximum dexterity index of the robot. Under the optimization algorithm, a simulation of grinding a vane is included and comparison of grinding workspace is done before and after the tool frame optimization. By the algorithm, the grinding workspace can be enlarged. Moreover the dynamic tool frame can be considered to add one degree-of-freedom to the grinding kinematical chain, which provides the theoretical support for the improvement of robotic dexterity for the complex surface grinding.

Code Transform Model Producing High-Performance Program

This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer(GPT-2)reasonably,improving the program execution performance significantly.Besides,a theoretical estimation in statistics has given the minimum number of generated programs as required,which guarantees to find the best one within them.The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code.In addition to GPT-2,this study develops the variational Simhash algorithm to check the code similarity between sample program and newly generated program,and conceives the piecewise longest common subsequence algorithm to examine the execution’s conformity from the two programs mentioned above.The code similarity check deducts the redundant generated programs,and the output conformity check finds the best-performing generative program.In addition to texts,the proposed approach can also prove the other media,including images,sounds,and movies.As a result,the newly generated program outperforms the sample program significantly because the number of code lines reduces 27.21%,and the program execution time shortens 24.62%.

Coordinated Cyber-Physical equipment planning for distributed generation based on chance constrained

With development of distributed generation(DG),configuration of optimization equipment is crucial for absorbing excess electricity and stabilizing fluctuations.This study proposes a two-layer configuration strategy coordinates active cyber control and the physical energy storage(ES)system.First,an upper economic model is developed.Based on chance-constrained programming,an operation model accounts for inherent uncertainty are then developed.Under constraint of voltage risk level,a lower operation model is developed.Finally,a solution based on differential evolution is provided.An IEEE 33 bus system simulation was used to validate efficacy of model.The effects of risk level,equipment price,and chance-constrained probability were analyzed,providing a foundation for power consumption and expansion of cyber-physical systems.

Incremental Network Programming for Wireless Sensors

We present an incremental network programming mechanism which reprograms wireless sensors quickly by transmitting the incremental changes using the Rsync algorithm;we generate the difference of the two program images allowing us to distribute only the key changes. Unlike previous approaches, our design does not assume any prior knowledge of the program code structure and can be applied to any hardware platform. To meet the resource constraints of wireless sensors, we tuned the Rsync algorithm which was originally made for updating binary files among powerful host machines. The sensor node processes the delivery and the decoding of the difference script separately making it easy to extend for multi-hop network programming. We are able to get a speed-up of 9.1 for changing a constant and 2.1 to 2.5 for changing a few lines in the source code.

Solving open vehicle problem with time window by hybrid column generation algorithm

This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

Study on Test Compaction in High-Level Automatic Test Pattern Generation (ATPG) Platform

Advancements in semiconductor technology are making gate-level test generation more challenging. This is because a large amount of detailed structural information must be processed in the search process of automatic test pattern generation (ATPG). In addition, ATPG needs to deal with new defects caused by process variation when IC is shrinking. To reduce the computation effort of ATPG, test generation could be started earlier at higher abstraction level, which is in line with top-down design methodology that has become more popular nowadays. In this research, we employ Chen’s high-level fault model in the high-level ATPG. Besides shorter ATPG time as shown in many previous works, our study showed that high-level ATPG also contributes to test compaction. This is because most of the high-level faults correlate with the gate-level collapsed faults especially at input/output of the modules in a circuit. The high-level ATPG prototype used in our work is mainly composed by constraint-driven test generation engine and fault simulation engine. Experimental result showed that more reduced/compact test set can be generated from the high-level ATPG.

A simple and accurate method for measuring program/erase speed in a memory capacitor structure

With the merits of a simple process and a short fabrication period, the capacitor structure provides a convenient way to evaluate memory characteristics of charge trap memory devices. However, the slow minority carrier generation in a capacitor often makes an underestimation of the program/erase speed. In this paper, illumination around a memory capacitor is proposed to enhance the generation of minority carriers so that an accurate measurement of the program/erase speed can be achieved. From the dependence of the inversion capacitance on frequency, a time constant is extracted to quantitatively characterize the formation of the inversion layer. Experimental results show that under a high enough illumination, this time constant is greatly reduced and the measured minority carrier-related program/erase speed is in agreement with the reported value in a transistor structure.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

Power Generation Expansion Planning Using an Interior Point with Cutting Plane （IP/CP） Method

The generation expansion planning is one of complex mixed-integer optimization problems, which involves a large number of continuous or discrete decision variables and constraints. In this paper, an interior point with cutting plane （IP/CP） method is proposed to solve the mixed-integer optimization problem of the electrical power generation expansion planning. The IP/CP method could improve the overall efficiency of the solution and reduce the computational time. Proposed method is combined with the Bender＇s decomposition technique in order to decompose the generation expansion problem into a master investment problem and a slave operational problem. The numerical example is presented to compare with the effectiveness of the proposed algorithm.

Wind Generation in the Argentine Power System

Energy policy is an essential part of the economy and the society. In some countries, there is a lack of a regulatory framework, which must be clear and practicable to allow new technologies to compete with the conventional way of generation. This is the problem in Argentine, the lack of a regulatory framework that can regulate the insertion of wind energy into the Argentine power system （SADI）. In this paper, a review of typical incentives for the installation of wind farms in the world, and a review of some laws and policies in Argentine are presented. Also financial and economic issues that are related to the installation of wind farms are analyzed, and some recommendations related to the topics are presented in this paper.

从故事生成程序到大语言模型:人工智能编剧创作的历史与实践

人工智能生成内容(AI Generated Content,简称AIGC)电影包括内涵丰富的多种维度,从编剧角度可以分为AI生成故事(AI Generated Storyline,简称AIGSL)与AI生成剧本(AI Generated Screenplay,简称AIGSP)。AIGSL研究始于20世纪60年代初,按照创作模式可以分为基于结构模型的故事生成、基于规则的故事生成和基于机器学习的故事生成三类程序。2022年底以来,以Chat GPT为代表的大语言模型将AIGC的内容从简单的AIGSL拓展到了AIGSP领域,同时催生了基于大语言模型的专用故事与剧本生成工具。大语言模型的故事和剧本生产存在事实错误、陈词滥调以及主观偏见等天然缺陷,但AIGSL与AIGSP已经开始实现产业化。目前的人工智能编剧创作绝大多数并不完全由AI创作,属于弱人工智能领域。中国电影工作者与软件工程师有必要利用机器学习等AI技术,学习中国传统叙事文本并总结其叙事风格,为具有中国叙事特色的编剧创作提供智能助力。

基于新型线边集成超市的周期性物料配送优化

为结合传统线边储料和成套供料的优势,有效改善汽车混流装配线的送料机制,引入一种新型线边集成超市物料配送系统,并对送料工人进行物料配送工位分配和周期性配送优化。首先,对相互关联的工位分配和周期性配送问题进行描述,并以最小化送料工人固定成本和物料配送成本为目标建立数学模型。其次,结合模型提出引理定理、构建嵌套启发式动态规划方法获取小规模问题的精确解,对于中大规模问题,构建改进型和声搜索算法进行求解。在算法设计中,通过反复拆分、合并和声记忆库加快算法的搜索速度,并融入邻域搜索、交叉变异等操作以扩大和声搜索空间、避免传统和声搜索算法早熟收敛、易陷入局部最优等缺点。最后,通过仿真实验与其他改进算法进行对比,验证了该算法运行速度快、搜索结果优,可有效解决物料配送优化问题。

多窗口屏幕格式生成器的设计

介绍一个采用面向对象的思想和窗口技术实现的屏幕格式生成器SFG,使用SFG能生成用户界面友好的屏幕I/O程序,提高应用系统开发效率,保证软件质量。

Predicting Effectiveness of Generate-and-Validate Patch Generation Systems Using Random Forest

One way to improve practicability of automatic program repair（APR） techniques is to build prediction models which can predict whether an application of a APR technique on a bug is effective or not. Existing prediction models have some limitations. First, the prediction models are built with hand crafted features which usually fail to capture the semantic characteristics of program repair task. Second, the performance of the prediction models is only evaluated on Genprog, a genetic-programming based APR technique. This paper develops prediction models, i.e., random forest prediction models for SPR, another kind of generate-and-validate APR technique, which can distinguish ineffective repair instances from effective repair instances. Rather than handcrafted features, we use features automatically learned by deep belief network（DBN） to train the prediction models. The empirical results show that compared to the baseline models, that is, all effective models, our proposed models can at least improve the F1 by 9% and AUC（area under the receiver operating characteristics curve） by 19%. At the same time, the prediction model using learned features at least outperforms the one using hand-crafted features in terms of F1 by 11%.

A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach. © 2017 Chinese Association of Automation.

Data Model Transformation in CAD System for Multi View Modeling

This paper describes multi view modeling and data model transformation for the modeling. We have proposed a reference model of CAD system generation, which can be applied to various domain specific languages. However, the current CAD system generation cannot integrate data of multiple domains. Generally each domain has its own view of products. For example, in the domain of architectural structure, designers extract the necessary data from the data in architecture design. Domain experts translate one view into another view beyond domains using their own brains.The multi view modeling is a way to integrate product data of multiple domains, and make it possible to translate views among various domains by computers.

Proving total correctness and generating preconditions for loop programs via symbolic-numeric computation methods

We present a symbolic-numeric hybrid method, based on sum-of-squares （SOS） relaxation and rational vec- tor recovery, to compute inequality invariants and ranking functions for proving total correctness and generating pre- conditions for programs. The SOS relaxation method is used to compute approximate invariants and approximate rank- ing functions with floating point coefficients. Then Gauss- Newton refinement and rational vector recovery are applied to approximate polynomials to obtain candidate polynomials with rational coefficients, which exactly satisfy the conditions of invariants and ranking functions. In the end, several exam- ples are given to show the effectiveness of our method.

一个具有图形界面的MIS菜单生成器的设计

描述一个ＭＩＳ菜单生成器的设计原理及方法．引入构成菜单所需的各种参数意义，介绍生成器组成和功能、主要技术和特点．用此生成器能生成各种不同风格的、具有图形界面的、能独立运行的、完整的菜单源程序．