Communication-saving design by stochastic event triggers

This paper proposes a design framework of sensor communication by using the stochastic event triggers, which aims at best saving the communication resources. The system to be considered is as follows: a sensor takes measurements of the states of a dynamic process and sends the information to a remote estimator, and the estimator computes the state estimates for the dynamic process. To save communication resources, a set of stochastic event triggers on the measurements are assigned to the sensor. At each sampled time, when no trigger is triggered, the sensor sends nothing to the estimator; when one of them is triggered, the sensor sends the identity code of the corresponding trigger. It is shown that once the estimator receives the identity of the trigger, it is equivalent for the estimator to receiving a measurement from a certain virtual sensor. Based on it, the system performance under the proposed communication is analyzed, and the design of specific models is considered. Examples are presented to demonstrate the proposed communication design.     

触发器在图书资料智能管理系统中的应用

为提高触发器的使用效率,对触发器的原理、运行机制和作用进行了剖析、分解,分析了SQL Server2008中两个相关的临时表,结合图书资料智能管理系统阐述了如何应用触发器解决实际问题,探讨了触发器技术在保证数据库完整性中的应用。     

基于触发词优先级的事件抽取研究

本文将触发词分为时间类和非时间类,对触发词提取算法进行改进,以一定量导电塑料行业新闻为基础语料构建两类触发词词表,并采取时间类触发词优先的事件句识别策略。基于该触发词词表对导电塑料和太阳能行业新闻语料进行事件句识别算法有效性实验,开放测试的召回率和准确率分别超过98%和95%。该结果表明：将触发词进行基于时间特性的分类,并优先使用时间类触发词提取事件句,能取得显著的效果。     

Cross-document event clustering using knowledge mining from co-reference chains

Unifying terminology usages which captures more term semantics is useful for event clustering. This paper proposes a metric of normalized chain edit distance to mine, incrementally, controlled vocabulary from cross-document co-reference chains. Controlled vocabulary is employed to unify terms among different co-reference chains. A novel threshold model that incorporates both time decay function and spanning window uses the controlled vocabulary for event clustering on streaming news. Under correct co-reference chains, the proposed system has a 15.97% performance increase compared to the baseline system, and a 5.93% performance increase compared to the system without introducing controlled vocabulary. Furthermore, a Chinese co-reference resolution system with a chain filtering mechanism is used to experiment on the robustness of the proposed event clustering system. The clustering system using noisy co-reference chains still achieves a 10.55% performance increase compared to the baseline system. The above shows that our approach is promising.     

中文事件抽取研究文献之算法效果分析

事件抽取是指识别文本中描述在某个时间(或时间段),某个地点或地区,由一个或多个角色参与的某动作的事件。首先对我国关于事件抽取研究的文献进行了总结,给出事件抽取的主要方法及模型。并针对文献中对这些事件抽取方法的效果进行统计分析,探讨各种事件抽取方法或模型的效果及适用性。经过对现有研究文献的统计,结论为:当前有关事件抽取的研究仍在继续,主要集中于金融资讯、会议信息、突发事件、个人简历等来自网页、微博微信等自媒体信息或军事法律等专业文件的事件抽取,所采用的算法包括SVM、CRF、ME、模式匹配、聚类算法等;CRF算法应用与个人简历事件抽取效果最好,采用模式匹配算法的有效文献量相对较多,触发词方法的综合效果较优于模式匹配算法,但较多领域存在触发词算法的查全率较低的问题。     

A systemic functional linguistics approach to implicit entity recognition in tweets

The identification of knowledge graph entity mentions in textual content has already attracted much attention. The major assumption of existing work is that entities are explicitly mentioned in text and would only need to be disambiguated and linked. However, this assumption does not necessarily hold for social content where a significant portion of information is implied. The focus of our work in this paper is to identify whether textual social content include implicit mentions of knowledge graph entities or not, hence forming a two-class classification problem. To this end, we adopt the systemic functional linguistic framework that allows for capturing meaning expressed through language. Based on this theoretical framework we systematically introduce two classes of features, namely syntagmatic and paradigmatic features, for implicit entity recognition. In our experiments, we show the utility of these features for the task, report on ablation studies, measure the impact of each feature subset on each other and also provide a detailed error analysis of our technique.     

基于语义聚类的关键词抽取方法

【目的/意义】关键词抽取的本质是找到能够表达文档核心语义信息的关键词汇,因此使用语义代替词语进行分析更加符合实际需求。本文基于TextRank词图模型,利用语义代替词语进行分析,提出了一种基于语义聚类的关键词抽取方法。【方法/过程】首先,将融合知网(HowNet)义原信息训练的词向量聚类,把词义相近的词语聚集在一起,为各个词语获取相应的语义类别。然后,将词语所属语义类别的窗口共现频率作为词语间的转移概率计算节点得分。最后,将TF-IDF值与节点得分进行加权求和,对关键词抽取结果进行修正。【结果/结论】从整体的关键词抽取结果看,本文提出的关键词抽取方法在抽取效果上有一定提升,相比于TextRank算法在准确率P,召回率R以及F值上分别提升了12.66%、13.77%、13.16%。【创新/局限】本文的创新性在于使用语义代替词语,从语义层面对相关性网络进行分析。同时,首次引入融合知网义原信息的词向量用于关键词抽取工作。局限性在于抽取方法依赖知网信息,只适用于中文文本抽取。     

SQL Server 2005中触发器的应用

触发器是一种保证数据完整性的方法,它是一种特殊的存储过程,本文简述了SQL Server 2005中的重种触发器对象：After,DDL和Instead of触发器的概念、工作原理,并给出了应用实例。     

Local word vectors guiding keyphrase extraction

Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors), i.e., embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and therefore can help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state-of-the-art unsupervised keyphrase extraction methods.     

Exponential synchronization of multilayer networks with white-noise-based coupling via intermittent periodic event-triggered control

In this paper, the mean-square exponential synchronization of stochastic multilayer networks with white-noise-based time-varying coupling is investigated via intermittent dynamic periodic event-triggered control. The existence of a dynamic term can reduce the number of event triggers. Furthermore, by introducing periodic sampling mechanism, a minimum inter-execution time is guaranteed to avoid Zeno phenomenon. Additionally, by employing Lyapunov method, graph theory, and stochastic analysis techniques, synchronization criteria for multilayer networks under intermittent dynamic periodic event-triggered control are established. To clarify the process of synchronization of multilayer networks, a brief framework is developed on the basis of Tajan’s algorithm. Ultimately, theoretical results are applied into Chua’s circuits and corresponding numerical simulations are given to illustrate the effectiveness and feasibility of the results.     

框架与Petri网相结合的中文情境知识表示方法研究

本文选取汉语框架语义知识库中的框架信息作为情境知识的内容来源,采用善于处理异步状态变迁和并发事件的计算机语言Petri网对其进行形式化表示,使之成为计算机可读、可理解的情境知识资源,从而有利于实现智能化语义推理功能.     

Few-shot entity linking of food names

Entity linking (EL), the task of automatically matching mentions in text to concepts in a target knowledge base, remains under-explored when it comes to the food domain, despite its many potential applications, e.g., finding the nutritional value of ingredients in databases. In this paper, we describe the creation of new resources supporting the development of EL methods applied to the food domain: the E.Care Knowledge Base (E.Care KB) which contains 664 food concepts and the E.Care dataset, a corpus of 468 cooking recipes where ingredient names have been manually linked to corresponding concepts in the E.Care KB. We developed and evaluated different methods for EL, namely, deep learning-based approaches underpinned by Siamese networks trained under a few-shot learning setting, traditional machine learning-based approaches underpinned by support vector machines (SVMs) and unsupervised approaches based on string matching algorithms. Combining the strengths of each of these approaches, we built a hybrid model for food EL that balances the trade-offs between performance and inference speed. Specifically, our hybrid model obtains 89.40% accuracy and links mentions at an average speed of 0.24 seconds per mention, whereas our best deep learning-based model, SVM model and unsupervised model obtain accuracies of 86.99%, 87.19% and 87.43% at inference speeds of 0.007, 0.66 and 0.02 seconds per mention, respectively.     

AugPrompt: Knowledgeable augmented-trigger prompt for few-shot event classification

Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires abundant held-out classes for model learning and selection. Thus we propose to deal with the low-resource problem by utilizing prompts. Further, existing methods suffer from severe trigger biases that may result in ignorance of the context. That is, the correct classifications are gained by looking at only the triggers, which hurts the model’s generalization ability. Thus, we propose a knowledgeable augmented-trigger prompt FSEC framework (AugPrompt), which can overcome the bias issues and alleviates the classification bottleneck brought by insufficient data. In detail, we first design an External Knowledge Injection (EKI) module to incorporate an external knowledge base (Related Words) for trigger augmentation. Then, we propose an Event Prompt Generation (EPG) module to generate appropriate discrete prompts for initializing the continuous prompts. After that, we propose an Event Prompt Tuning (EPT) module to automatically search prompts in the continuous space for FSEC and finally predict the corresponding event types of the inputs. We conduct extensive experiments on two public English datasets for FSEC, i.e., FewEvent and RAMS. The experimental results show the superiority of our proposal over the competitive baselines, where the maximum accuracy increase compared to the strongest baseline reaches 10.8%.     

创业自我效能感与创新机会识别：一个有调节的中介模型

创业自我效能感是创新机会识别的促进因素这一观点可能夸大了创业自我效能感的好处而忽视了其潜在的成本。为此,关注创业自我效能感在机会识别过程中的“黑暗”面,并将信息扫描频率引入创新机会识别过程,基于145名中国创业者的调查数据,构建一个有调节的中介模型来考察创业自我效能感、信息扫描频率、创业经验与创新机会识别之间的关系。结果显示：创业自我效能感对信息扫描频率有负向影响,而信息扫描频率的降低不利于创新机会的识别;信息扫描频率在创业自我效能感与创新机会识别的关系中发挥负向中介作用,而创业经验可以缓解信息扫描频率的负向中介效应。基于此,提出新创企业管理者应正确看待创业自我效能感对创新机会识别的作用机制,创业者应积极进行信息扫描并更好地利用创业经验等对策建议,以此启发创业者在实践中更好地识别创新机会。     

Secure state estimation for event-triggered cyber-physical systems against deception attacks

Nowadays, cyber-physical systems (CPSs) have been widely used in various fields due to their powerful performance and low cost. The cyber attacks will cause security risks and even huge losses according to the universality and vulnerability of CPSs. As a typical network attack, deception attacks have the features of high concealment and strong destructiveness. Compared with the traditional deception attack models with a constant value, a deception attack with random characteristics is introduced in this paper, which is difficult to identify. In order to defend against such deception attacks and overcome energy constraints in CPSs, the secure state estimation and the event-triggered communication mechanism without feedback information are co-considered to reconcile accuracy of estimation and energy consumption. Firstly, an event-triggered augmented state estimator is proposed for secure state estimation and attack identification. Then, under the ideology of equivalence, the augmented state estimator is derived as a concise two-stage estimator with reduced order. The two-stage estimator can perform the secure state estimation and attack identification respectively. The estimators ensure the accuracy of attack identification well since not treating attack information as the trigger event. Afterward, the comparison of the computational complexity of these two algorithms is analyzed. Finally, an example of a target tracking system is supplied to prove the effectiveness and efficiency of the proposed algorithm.     

迎合性高新技术企业认定的识别及经济后果——基于KY公司的案例研究

基于迎合性动机视角,选取KY公司作为案例研究对象,综合运用AHP-模糊综合评价法和财务指标分析方法,探究迎合性高新技术企业认定的识别及经济后果.研究发现,迎合性高新技术认定对KY公司的创新绩效无明显促进作用,对其财务绩效的正向影响亦较为有限,说明迎合性动机驱动的高新技术企业认定无法达到预期政策效果.最后,本文从政府和企业维度提出若干建议,以期为提升我国高新技术企业创新绩效和高新技术企业认定政策效果提供政策参考.     

NATSUM: Narrative abstractive summarization through cross-document timeline generation

A new approach to narrative abstractive summarization (NATSUM) is presented in this paper. NATSUM is centered on generating a narrative chronologically ordered summary about a target entity from several news documents related to the same topic. To achieve this, first, our system creates a cross-document timeline where a time point contains all the event mentions that refer to the same event. This timeline is enriched with all the arguments of the events that are extracted from different documents. Secondly, using natural language generation techniques, one sentence for each event is produced using the arguments involved in the event. Specifically, a hybrid surface realization approach is used, based on over-generation and ranking techniques. The evaluation demonstrates that NATSUM performed better than extractive summarization approaches and competitive abstractive baselines, improving the F1-measure at least by 50%, when a real scenario is simulated.     

基于Web评论挖掘的动态竞争情报分析研究(下)——算法设计与实验分析

针对当前竞争情报分析算法无法有效地处理结构类型多样的Web评论信息,研究并设计了基于Web评论挖掘的动态竞争情报分析算法进行语义层面的情报分析,获取深层次的动态竞争情报。该算法主要由评论内容识别与抽取、语义关联挖掘、文本语义极性分析3个部分组成。实验结果表明,该算法能够显著地提高情报分析的准确率和效率,实现深层次的评论挖掘和情报知识发现。     

演绎、归纳与溯因———从信息哲学的观点看

推理实质上是认知信息的更新方式。从信息哲学的角度,可以为演绎、归纳与溯因等三种推理方式划定界限。除了可能世界语义学,使用邻域模型也可以为认知信息的更新构造模型。有两种更新方式：交集模型和子集模型。利用交集模型可以对演绎推理作出信息论的解释。归纳推理的结论是带强度的信念。子集模型可以对科学发现的过程作出解释,从而区分演绎、归纳和溯因。邻域语义学揭示的两种信息更新方式是不同的,这是信息更新的逻辑动态学方面的新发现。     

基于专业术语提取的中文分词方法

针对在科技文献中,未登录词等相关专业术语其变化多端,在中文分词中难以识别,影响了专业领域文章的分词准确度,结合实际情况给出了一种基于专业术语提取的中文分词方法。通过大量特定领域的专业语料库,基于互信息和统计的方法,对文中的未登录词等专业术语进行提取,构造专业术语词典,并结合通用词词典,利用最大匹配方法进行中文分词。经实验证明,该分词方法可以较准确的抽取出相关专业术语,从而提高分词的精度,具有实际的应用价值。