Poetry I: Teaching verse with CAI

The teaching of literature through CAI raises problems of both a linguistic and instructional nature; student involvement and creativity in studying literature, and especially poetry, is difficult to build into a computer-based lesson. We have confronted these difficulties in the lessonPoetry I, which introduces undergraduates to basic concepts of poetic verse in a design using screen display, speech synthesis, and verse processing to maximize interactivity and student involvement.The lesson contains instructional modules which include the student's composition of a limerick. Computational processing of the limerick's text enables the program to offer guidance as the student composes and revises the verse and gains first-hand experience with metrical language. The significant problems inherent in processing verse are addressed by adapting Digital Equipment Corporation's DECTalk speech synthesizer as an engine converting natural language verse text into accessible strings of phonemic symbols. Although the verse processor cannot scan verse, it can interpret DECTalk's symbols reliably enough to successfully stimulate students' thinking about their verse compositions.Preliminary responses to the program have been favorable and demonstrate its effectiveness in involving students more deeply in learning about poetic verse. This suggests that the techniques of verse processing prototyped inPoetry I might be usefully extended to other types of verse and levels of study.W. Webster Newbold is an Assistant Professor of English at Ball State University, Muncie, Indiana, USA, who has recently served as English Department Computer Coordinator. His research interests include computer-assisted instruction and computer-based composition.Herbert F. W Stahlke is a Professor of English, specializing in linguistics, and currently serves as Associate Director for Academic Computing, Ball State University Computing Services. His research interests include computer-assisted instruction, natural language processing, and lexical databases.     

An optimal algorithm for the boundary of a cell in a union of rays

In this paper we study a cell of the subdivision induced by a union ofn half-lines (or rays) in the plane. We present two results. The first one is a novel proof of theO(n) bound on the number of edges of the boundary of such a cell, which is essentially of methodological interest. The second is an algorithm for constructing the boundary of any cell, which runs in optimal (n logn) time. A by-product of our results are the notions of skeleton and of skeletal order, which may be of interest in their own right.This work was partly supported by CEE ESPRIT Project P-940, by the Ecole Normale Supérieure, Paris, and by NSF Grant ECS-84-10902.This work was done in part while this author was visiting the Ecole Normale Supérieure, Paris, France.     

应用感知的数据中心网络多租户共享方法

在多租户数据中心,来自不同租户的应用程序共享并竞争使用网络资源.网络共享策略会对应用程序端到端的性能(如作业完成时间、吞吐量等)产生直接的影响.为了衡量租户应用程序的整体数据传输速率,本文引入进度(Progress)的指标.该指标被定义为租户应用程序在所有链路上经需求标准化后的最小带宽分配量,反映的是租户能够完成其数据传输的最慢速率.通过最大程度地提高租户进度,可以优化上层应用程序的执行时间等性能.先前的大多数工作都集中在实现网络共享的公平性、可预测性和效率之间的权衡,忽略了提高租户的长期进度.本文观察发现应用程序放置于租户所租赁的不同虚拟机上会形成不同的带宽需求分布,进而影响后续带宽分配所能够获得的最优进度.通过理论分析我们证明了获得所有租户最优进度的关键在于最小化网络瓶颈链路上的带宽需求.基于此,本文提出应用感知的网络多租户共享方法,通过联合优化任务放置和带宽分配的过程,该方法最大化所有租户的进度,并在优势资源公平性限制下最大化网络利用率.实验结果证明,与目前的最新带宽分配方法相比,本文将租户整体进度提高了85.6％～107.7％,网络链路利用率提高了71.2％～112.4％.     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Protect privacy of deep classification networks by exploiting their generative power

Machine Learning - Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a...     

Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

Neural Computing and Applications - Image classification tasks widely exist in many actual scenarios, including medicine, security, manufacture and finance. A major problem that hurts algorithm...     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...     

基于软件无线电的基带调制系统设计

针对通信系统中基带调制的多模式的需求,基于软件无线电原理,用FPGA设计了一种可实现2ASK、2FSK、BPSK三种调制模块,在Quartus II环境中利用VHDL语言完成了芯片程序的设计,并对其进行了仿真和功能测试,实验结果验证了用FPGA可快速实现2ASK、2FSK和BPSK基带调制功能,调制信号稳定可靠,可以用软件来控制调制方式,系统方案正确可行,而且具有良好的稳定性和灵活性.     

基于FPGA和ARM的弹上相位自动微调控制器设计与实现

为了提高相位自动微调控制器的控制性能,针对某型导弹,提出并实现了以ARM+ FPGA双微处理器为核心的硬件设计方案,采用了模糊PID控制算法,通过优化步进电机控制模式,提高了相位自动微调控制器的控制精度,将步进电机的调整时间由800 ms缩短到100 ms以内;该方案已成功应用于某型弹上系统,实践证明,该设计方案成本低、可扩展性强,能够满足系统控制需求,具有较高的实用性,大大提高了装备维护效率.