基于源代码插桩的C程序内存使用错误动态检测

针对C程序内存使用错误的预防和处理方法分基于静态分析和动态分析两类。静态分析工具研制复杂，目前还没有得到广泛使用的高效工具。动态分析又分为基于目标代码和源代码插桩分析两类。由于缺少完整的语法、语义信息，基于目标代码的分析有时对错误的捕捉、定性不够精确。基于源代码的分析由于其信息的完整可以达到对错误的最精确报告。本文对C程序中的内存使用错误进行了分析，并讨论了一个C程序内存错误动态检测工具的设计思想，给出了系统流程、关键数据结构、错误捕捉方法的设计。     

短时记忆与外语学习

短时记忆在外语学习中起着十分重要的作用。本文论述了短时记忆的特点及与长一时记忆、工作记忆的关系，探讨了短时记忆与外语学习的关系，最后提出了提高外语学习的一些方法。     

面向共享汽车的智能化调节座椅系统设计

文章设计了一种面向共享汽车的智能化调节座椅系统,该系统通过手机客户端APP输入用户体型特征信息,系统上传个人信息至云端并进行智能化数据计算,模拟出一个用户舒适的驾驶环境,使用该数据调动相应电机工作调整座椅等位置,用户也可根据自身的驾驶习惯,调整至最舒适、最安全的驾驶环境。驾驶结束时,车辆ECU记录此时座椅、后视镜和方向盘的位置数据信息并上传至云端服务器,存入该用户的资料库,下次使用时直接调用数据并调节车辆。该系统的使用将有助于共享汽车的推广,使用户的出行更便捷环保。     

Improved Parallel Three-List Algorithm for the Knapsack Problem without Memory Conflicts

Based on the two-list algorithm and the parallel three-list algorithm, an improved parallel three-list algorithm for knapsack problem is proposed, in which the method of divide and conquer, and parallel merging without memory conflicts are adopted. To find a solution for the n-element knapsack problem, the proposed algorithm needs O（2^3n/8） time when O（2^3n/8） shared memory units and O（2^n/4） processors are available. The comparisons between the proposed algorithm and 10 existing algorithms show that the improved parallel three-fist algorithm is the first exclusive-read exclusive-write （EREW） parallel algorithm that can solve the knapsack instances in less than O（2^n/2） time when the available hardware resource is smaller than O（2^n/2） , and hence is an improved result over the past researches.     

Transit-oriented autonomous vehicle operation with integrated demand-supply interaction

Autonomous vehicles (AVs) represent potentially disruptive and innovative changes to public transportation (PT) systems. However, the exact interplay between AV and PT is understudied in existing research. This paper proposes a systematic approach to the design, simulation, and evaluation of integrated autonomous vehicle and public transportation (AV + PT) systems. Two features distinguish this research from the state of the art in the literature: the first is the transit-oriented AV operation with the purpose of supporting existing PT modes; the second is the explicit modeling of the interaction between demand and supply.We highlight the transit-orientation by identifying the synergistic opportunities between AV and PT, which makes AVs more acceptable to all the stakeholders and respects the social-purpose considerations such as maintaining service availability and ensuring equity. Specifically, AV is designed to serve first-mile connections to rail stations and provide efficient shared mobility in low-density suburban areas. The interaction between demand and supply is modeled using a set of system dynamics equations and solved as a fixed-point problem through an iterative simulation procedure. We develop an agent-based simulation platform of service and a discrete choice model of demand as two subproblems. Using a feedback loop between supply and demand, we capture the interaction between the decisions of the service operator and those of the travelers and model the choices of both parties. Considering uncertainties in demand prediction and stochasticity in simulation, we also evaluate the robustness of our fixed-point solution and demonstrate the convergence of the proposed method empirically.We test our approach in a major European city, simulating scenarios with various fleet sizes, vehicle capacities, fare schemes, and hailing strategies such as in-advance requests. Scenarios are evaluated from the perspectives of passengers, AV operators, PT operators, and urban mobility system. Results show the trade off between the level of service and the operational cost, providing insight for fleet sizing to reach the optimal balance. Our simulated experiments show that encouraging ride-sharing, allowing in-advance requests, and combining fare with transit help enable service integration and encourage sustainable travel. Both the transit-oriented AV operation and the demand-supply interaction are essential components for defining and assessing the roles of the AV technology in our future transportation systems, especially those with ample and robust transit networks.     

电子海图快速显示方法研究

根据电子海图的数据特点和单幅电子海图调图显示原理，提出了一种多幅电子海图拼图模式下的快速显示方法。该方法运用了计算机内存位图技术和内存缓冲技术，提高了电子海图显示速度和显示效果。该方法已被运用到某新型电子海图系统，并被证明能较好地解决拼图模式下无级缩放和快速漫游问题。     

谈口译教学中的技能训练

文章认为英语专业口译课教学的重点应以技能训练为中心,并辅之以语言训练,探讨了培养学生综合素质的问题,同时介绍了几种常见的技巧:记忆技巧、笔记技巧、公众演说技巧等。     

A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting

Short-term traffic volume forecasting represents a critical need for Intelligent Transportation Systems. This paper develops a novel forecasting approach inspired by human memory, called the spinning network (SPN). The approach is then used for short-term traffic volume forecasting, utilizing a data set compiled from real-world traffic volume data obtained from the Hampton Roads traffic operations center in Virginia. To assess the accuracy of the SPN approach, its performance is compared to two other approaches, namely a back propagation neural network and a nearest neighbor approach. The transferability of the SPN approach and its ability to forecast for longer time periods into the future is also assessed. The results of the performance testing conducted in this paper demonstrates the superior predictive accuracy and drastically lower computational requirements of the SPN compared to either the neural network or the nearest neighbor approach. The tests also confirm the ability of the SPN to predict traffic volumes for longer time periods into the future, as well as the transferability of the approach to other sites.     

Assessing public opinions of and interest in new vehicle technologies: An Austin perspective

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles.Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.