Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator

The publication indicator of the Finnish research funding system is based on a manual ranking of scholarly publication channels. These ranks, which represent the evaluated quality of the channels, are continuously kept up to date and thoroughly reevaluated every four years by groups of nominated scholars belonging to different disciplinary panels. This expert-based decision-making process is informed by available citation-based metrics and other relevant metadata characterizing the publication channels. The purpose of this paper is to introduce various approaches that can explain the basis and evolution of the quality of publication channels, i.e., ranks. This is important for the academic community, whose research work is being governed using the system. Data-based models that, with sufficient accuracy, explain the level of or changes in ranks provide assistance to the panels in their multi-objective decision making, thus suggesting and supporting the need to use more cost-effective, automated ranking mechanisms. The analysis relies on novel advances in machine learning systems for classification and predictive analysis, with special emphasis on local and global feature importance techniques.     

Learning-based summarisation of XML documents

Documents formatted in eXtensible Markup Language (XML) are available in collections of various document types. In this paper, we present an approach for the summarisation of XML documents. The novelty of this approach lies in that it is based on features not only from the content of documents, but also from their logical structure. We follow a machine learning, sentence extraction-based summarisation technique. To find which features are more effective for producing summaries, this approach views sentence extraction as an ordering task. We evaluated our summarisation model using the INEX and SUMMAC datasets. The results demonstrate that the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that the learnable system is also effective and well-suited to the task of summarisation in the context of XML documents. Our approach is generic, and is therefore applicable, apart from entire documents, to elements of varying granularity within the XML tree. We view these results as a step towards the intelligent summarisation of XML documents.

雷尼绍测头系统在西门子840D数控系统上的应用

数控机床在线检测技术的发展,为数控加工过程的质量监控提供了一套更为高效的方法。本文在西门子840D系统数控机床的加工过程中,利用雷尼绍OMP60测头系统对工件、夹具等进行在线检测,通过测量值的分析、计算并反馈,实现对被加工工件定位状态、加工尺寸的实时监控及自动补偿,从而大大提高数控机床加工效率,确保工件加工质量。     

基于MATLAB的无刷双馈风力发电机的控制策略研究

在传统风力发电的基础上,以作为风力发电机用的无刷双馈电机为主要研究对象,进行了深入的研究,研究了无刷双馈电机的转子侧和网侧PWM变换器的控制策略,建立转子侧和网侧PWM变换器控制策略的结构模型,把功率绕组和控制绕组分别建立在各自的同步坐标系下,分别进行磁场定向和电压定向矢量控制,从而简化了数学模型,获得了适合的控制策略。最后采用Matlab/Simulink仿真软件建立相应的控制系统仿真模型,模拟三种情景,进行仿真实验,分析实验结果。     

基于核聚类的K-均值聚类（英文稿）

为处理线性不可分、结构复杂的数据集,提出基于核聚类的K 均值聚类(Kernel Clustering based K means Clustering,KCKC).该方法先在原始空间中对模式进行聚类,再由径向基函数(Radial Basis Function, RBF)核把它们映射到核空间,从而保持大部分模式之间的关系.把提出的方法应用到基于RBF的神经网络(RBF based Neural Network,RBFNN)、基于RBF的支持向量机(RBF based Support Vector Machine, RBFSVM)和核最近邻分类器(Kernel Nearest Neighbor Classifier,KNNC)中,结果表明本文提出的算法可以生成更有效的核,节省在核空间中的核生成时间,避免核数目设置的敏感性,并提高分类性能.     

电力线数字载波技术的应用及发展

载波通信投资少,见效快,是电力调度通信的重要手段,而数字载波是必然的发展方向。为此,介绍了数字载波的优点、技术现状及其实际应用,并针对其不足之处,从载波网络管理、动态码速传输和载波频率分配及增加通信带宽等方面提出了改进建议。     

机器思维及一种新的思维方式分类法

机器思维问题是人工智能的基本问题之一⒚本文从思维方式这一层次上讨论了人（大脑）的思维与机器思维的关系及差别⒚提出了一种新的思维方式分类法,得出一些有意义的结论⒚     

市场竞争与大重型机床产品设计模块化

概述了大重型机床生产的特点,设想了模块化设计的特点和应用,分析了机床产品设计模块化的优势,指出了市场竞争需要产品设计模块化。     

Semantic matching in machine reading comprehension: An empirical study

Machine reading comprehension (MRC) is a challenging task in the field of artificial intelligence. Most existing MRC works contain a semantic matching module, either explicitly or intrinsically, to determine whether a piece of context answers a question. However, there is scant work which systematically evaluates different paradigms using semantic matching in MRC. In this paper, we conduct a systematic empirical study on semantic matching. We formulate a two-stage framework which consists of a semantic matching model and a reading model, based on pre-trained language models. We compare and analyze the effectiveness and efficiency of using semantic matching modules with different setups on four types of MRC datasets. We verify that using semantic matching before a reading model improves both the effectiveness and efficiency of MRC. Compared with answering questions by extracting information from concise context, we observe that semantic matching yields more improvements for answering questions with noisy and adversarial context. Matching coarse-grained context to questions, e.g., paragraphs, is more effective than matching fine-grained context, e.g., sentences and spans. We also find that semantic matching is helpful for answering who/where/when/what/how/which questions, whereas it decreases the MRC performance on why questions. This may imply that semantic matching helps to answer a question whose necessary information can be retrieved from a single sentence. The above observations demonstrate the advantages and disadvantages of using semantic matching in different scenarios.     

Detecting racial stereotypes: An Italian social media corpus where psychology meets NLP

The generation of stereotypes allows us to simplify the cognitive complexity we have to deal with in everyday life. Stereotypes are extensively used to describe people who belong to a different ethnic group, particularly in racial hoaxes and hateful content against immigrants. This paper addresses the study of stereotypes from a novel perspective that involves psychology and computational linguistics both. On the one hand, it describes an Italian social media corpus built within a social psychology study, where stereotypes and related forms of discredit were made explicit through annotation. On the other hand, it provides some lexical analysis, to bring out the linguistic features of the messages collected in the corpus, and experiments for validating this annotation scheme and its automatic application to other corpora in the future. The main expected outcome is to shed some light on the usefulness of this scheme for training tools that automatically detect and label stereotypes in Italian.