航运业未完航次利润核算平台的设计与实现

根据新会计准则和报表披露要求,远洋运输企业需要根据合同进度采用完工百分比核算法对未完航次的收入和成本费用进行核算。本文通过企业的实际应用简要介绍了一种基于SAP(思爱普)系统的未完航次利润核算平台的设计思路和实现方法。通过ABAP(高级商务应用编程)的结构设计上载报文、匹配运营方式和算法变量并利用内表进行系数计算和凭证推导,最后自动完成平行记账,使财务会计和管理会计能同时体现未完航次的当期利润,从而满足了不同航运公司的核算需求和新局势的发展要求,解决了航运企业的实际问题。     

MODELS OF HUMAN COMMUNICATION: DIALOGUE WITH COMPUTER

A model of understanding texts of a natural language is presented, which consists of three levels. On the first level, the system uses only information in the given text. On the second level, it employs additional information from a knowledge base. On the third level, understanding is connected with some activity of the system stimulated by the available information.     

Defuzzification as a special way of dealing with retranslation

The problem of defuzzification is examined in this paper from a broader perspective as a special way of dealing with the general problem of retranslation. The paper includes an overview of different formulations of the problem of defuzzification, as well as an overview of methods that have been suggested in the literature for dealing with the problem. Our own approach to defuzzification, which is described in the paper in more details, is based on relevant measures of uncertainty-based information. This paper is a companion to our recent paper that addresses the general problem of retranslation in computing with perceptions (Martin and Klir 2006).     

计算机中用flash脚本语言实现下雨动画效果的方法

该文主要介绍了计算机中用flash脚本语言实现下雨动画效果的方法。     

《俄勒冈实验》模式原则实施效果评述

C?亚历山大教授在其出版的《俄勒冈实验》一书中，以俄勒冈大学为实验母体，界定出一种设计方式，以适用任何社区的总体规划，并归纳为6种主要原则。文章从现实角度出发，针对俄勒冈大学规划现状，提取6种原则中的模式原则并选择其中较为重要的几项模式对其论述进行比较研究，并依照现有实施状况进行一般性评价。对俄勒冈大学规划现状的研究将为我国高校下一阶段的校园规划和校园更新建设提供一定参考价值。     

Modeling dynamic construction work template from existing scheduling records via sequential machine learning

Today, construction planning and scheduling is almost always performed manually, by experienced practitioners. The knowledge of those individuals is materialized, maintained, and propagated through master schedules and look-ahead plans. While historical project schedules are available, manually mining their embedded knowledge to create generic work templates for future projects or revising look-ahead schedules is very difficult, time-consuming and error-prone. The rigid work templates from prior research are also not scalable to cover the inter and intra-class variability in historical schedule activities. This paper aims at fulfilling these needs via a new method to automatically learn construction knowledge from historical project planning and scheduling records and digitize such knowledge in a flexible and generalizable data schema. Specifically, we present Dynamic Process Templates (DPTs) based on a novel vector representation for construction activities where the sequencing knowledge is modeled with generative Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Our machine learning models are exhaustively tested and validated on a diverse dataset of 32 schedules obtained from real-world projects. The experimental results show our method is capable of learning planning and sequencing knowledge at high accuracy across different projects. The benefits for automated project planning and scheduling, schedule quality control, and automated generation of project look-aheads are discussed in detail.     

FarsBase-KBP: A knowledge base population system for the Persian Knowledge Graph

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabelled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system.     

The effects of animated pedagogical agents in an English-as-a-foreign-language learning environment

Animated pedagogical agents (APAs) have frequently been used as a powerful addition to learning environments, since APAs have been known to facilitate learning. APAs can present various features, such as voice, movements, gestures and pointing, and researchers have sought to verify specifically which features of agents effectively contribute to learning. Previous studies have studied these features by comparing different degrees of agent embodiment in the evaluation of the image effect (i.e., students learn more when learning systems have visual APAs), the embodied agent effect (i.e., fully embodied agents that deliver instruction aurally and use gestures to improve learning outcomes in text-only learning systems), the modality effect (i.e., oral instruction contributes to the learning process), and the expressiveness effect (i.e., fully embodied agents promote more effective learning than static ones). Some of these studies have investigated the image, embodied agent and modality effects in the same learning environment, but they were not the same studies that investigated the expressiveness effect. The expressiveness effect allows us to separate the movements of the agent from its other features, such as the agent's image, so investigating this effect is as important as investigating the other effects. We are not aware of any studies that investigated all of these four effects within the same learning system, nor that evaluated any of these effects in language learning environments. Accordingly, this paper describes the design, implementation, and analysis of an APA designed to evaluate the abovementioned effects. The APA was integrated into a computer-assisted language learning (CALL) system to teach English as a foreign language to Brazilian students. A total of 72 Brazilian undergraduate students were divided into four groups, each of which used a different version of the APA in the same CALL system: no agent, a voice-only agent, a static agent, or a fully embodied agent. We compared students’ gain scores (i.e., difference between pre- and posttest scores) across groups to evaluate each of the four effects. Though the outcomes of our study supported the presence of the embodied agent and modality effects, we were not able to demonstrate the image or expressiveness effects in the experiment. Our results indicate that the voice of the agent might contribute more positively to learning than movements, gestures and pointing.     

A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.