基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

基于桥连接的词典学习方法的语义解析

现阶段的语义解析方法大部分都基于组合语义,这类方法的核心就是词典。词典是词汇的集合,词汇定义了自然语言句子中词语到知识库本体中谓词的映射。语义解析一直面临着词典中词汇覆盖度不够的问题。针对此问题,该文在现有工作的基础上,提出了基于桥连接的词典学习方法,该方法能够在训练中自动引入新的词汇并加以学习,为了进一步提高新学习到的词汇的准确度,该文设计了新的词语—二元谓词的特征模板,并使用基于投票机制的核心词典获取方法。该文在两个公开数据集(WebQuestions和Free917)上进行了对比实验,实验结果表明,该文方法能够学习到新的词汇,提高词汇的覆盖度,进而提升语义解析系统的性能,特别是召回率。     

Mímir: An open-source semantic search framework for interactive information seeking and discovery

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.This paper introduces Mímir—an open-source framework for integrated semantic search over text, document structure, linguistic annotations, and formal semantic knowledge. Mímir supports complex structural queries, as well as basic keyword search.Exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices and term clouds. There is also an interactive retrieval interface, where users can save, refine, and analyse the results of a semantic search over time. The more well-studied precision-oriented information seeking searches are also well supported.The generic and extensible nature of the Mímir platform is demonstrated through three different, real-world applications, one of which required indexing and search over tens of millions of documents and fifty to hundred times as many semantic annotations. Scaling up to over 150 million documents was also accomplished, via index federation and cloud-based deployment.     

Word length and lexical activation: Longer is better.

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a system, long words should produce stronger lexical activation than short words, for 2 reasons: Long words provide more bottom-up evidence than short words, and short words are subject to greater inhibition due to the existence of more similar words. Four experiments provide evidence for this view. In addition, reaction-time-based partitioning of the data shows that long words generate greater activation that is available both earlier and for a longer time than is the case for short words. As a result, lexical influences on phoneme identification are extremely robust for long words but are quite fragile and condition-dependent for short words. Models of word recognition must consider words of all lengths to capture the true dynamics of lexical activation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

可拓知识空间和可拓知识网格模型(Ⅱ):可拓知识网格

第1部分中已介绍了可拓知识空间定义和相关操作，以及可拓知识空间关联函数计算方法。这一部分进一步建立可拓知识空间语义网、可拓知识网格和可拓知识网格服务，并给出实例和相关的分析讨论。     

"Interactive use of lexical information in speech perception": Correction to Connine and Clifton.

Reports an error in "Interactive use of lexical information in speech perception" by Cynthia M. Connine and Charles Clifton (Journal of Experimental Psychology: Human Perception and Performance, 1987[May], Vol 13[2], 291-299). In the aforementioned article, Figures 1 and 2 were inadvertently transposed. The figure on p. 294 is actually Figure 2, and the figure on p. 296 is actually Figure 1. The captions are correct as they stand. (The following abstract of the original article appeared in record 1987-23984-001.) Two experiments are reported that demonstrate contextual effects on identification of speech voicing continua. Experiment 1 demonstrated the infuence of lexical knowledge on identification of ambiguous tokens from word–nonword and nonword–word continua. Reaction times for word and nonword responses showed a word advantage only for ambiguous stimulus tokens (at the category boundary); no word advantage was found for clear stimuli (at the continua endpoints). Experiment 2 demonstrated an effect of a postperceptual variable, monetary payoff, on nonword–nonword continua. Identification responses were influenced by monetary payoff, but reaction times for bias-consistent and bias-inconsistent responses did not differ at the category boundary. An advantage for bias-consistent responses was evident at the continua endpoints. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What does the object decision task measure? Reflections on the basis of evidence from semantic dementia.

The authors investigated the impact of semantic knowledge on visual object analysis by assessing the performance of patients with semantic dementia on a different-views object matching test and on 2 object decision tests differing, for example, in whether the nonreal items were nonsense objects or chimeras of 2 real objects. On average, the patients scored normally on both the object matching and the object decision test including nonsense objects but were impaired on the object decision test including chimeras; this latter was also the only visual object test that correlated significantly with degree of semantic impairment. These findings demonstrate that object decision is not a single task or ability and that it is not necessarily independent of conceptual knowledge. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Contributions of Metaknowledge to Retrieval of Natural Categories in Semantic Memory.

Retrieving the answer to a general knowledge question has been shown to involve two metacognitive processes--a feeling-of-knowing that initiates the search of long-term memory and a willingness to continue searching until an answer can be confidently stated. To extend this model, college students were asked to retrieve as many members of 2 natural categories as they could in 1 min. Examination of the points at which they switched categories revealed that they searched longer in categories of higher potency, and they switched earlier when the other category was of higher potency. They also searched the first category longer when they were allowed to switch only once during a trial rather than as often as they wished. It was concluded that feeling-of-knowing maintained search of a category and also contributed to the willingness to continue searching, and the constraint on switching impacted the willingness to continue. (PsycINFO Database Record (c) 2010 APA, all rights reserved)