Structure-sensitive semantic matching for aggregate question answering over knowledge base

Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets.     

Ecological Planting Imperative: Functional Systems,Not Stylized Ecologies

The ability of landscape architectural projects to mitigate the worst effects of climate change will depend upon designed ecological systems. These systems will be built with plants. Despite the recognition of ecology as an essential driver of landscapes, the professionals of landscape architecture too often lack the knowledge and practical skills to create robust vegetative systems. New approaches and tools are required. This article outlines principles and methods for designing biodiverse plant systems for urban sites. Planting methods that increase species richness, functional diversity, and spatial complexity are emphasized as a way of developing more resilient plantings. Selecting species with similar evolutionary adaptions to stress, disturbance, and competition—as well as creating multi-layered compositions of diverse plant morphologies—allows designers to create compatible, long-lived plant mixes. To balance the increased visual complexity of diverse plant mixes, the article explores design techniques to make plantings more appealing to the public. The strategies explored here are based on the projects, experience, and research of Phyto Studio, a Washington, D.C. based studio. The methods build on work described in the author’s book, Planting in a Post-Wild World, an exploration of how to create designed plant communities.     

基于整体性视角的社区更新：框架、模式与案例 — —以佛山桂华邨社区为例

现行的政府单一主导、政府与开发 商合作的社区更新模式与业主的自主更新需 求产生了矛盾。针对现有研究对社区更新的 系统性缺乏关注的问题，本文从整体视角出 发，通过构建“参与主体—程序设计—法规 政策—资金调配”四维框架，分析现行社区 更新模式与业主自主更新需求产生冲突的内 在机制。在优化现有模式的基础上，提出三 种适应业主自主更新需求的理论模式。桂华 邨业主自主更新失败的实践案例证明了本文 所提出的四维框架的重要性。最后，从政府 视角对我国社区更新机制提出优化建议。     

Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.     

SINA: Semantic interpretation of user queries for question answering on interlinked data

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.     

Effectiveness of spatial representation in the formation of network communities: experimental study on community organizer

In this paper, we present and discuss Community Organizer, a system designed to support network communities. The main characteristic of Community Organizer is the use of spatial representations for the relationships among community members including the communications exchanged among these members. These spatial representations reflect the degree of closeness of interests and concerns among the members, and are intended to provide users with clues on how to form network communities. In order to investigate the effectiveness of the proposed spatial representations, we conducted experiments with two different versions of the software. One version offered meaningful spatial representations while the other version did not. The subjects who used the former software version felt a greater sense of ‘community’, enjoyed using the software more, and actively used it longer than the subjects using the latter software version (control condition). These results indicated that the proposed spatial representations are effective in supporting network community formation.     

计算机基础课题库系统中试卷生成模型研究

探讨了计算机基础课程题库系统中题型难度系数确定模型、试题在章节中的分布模型、各级难度题型分布模型，认为利用这些模型和随机函数能够确定计算机基础课程相应章节中相应等级难度的试题中抽取相应数量的试题，再利用数据库的检索功能实现试卷的自动生成。     

隧道工程施工中应注意的问题

通过对海南省东线高速公路大茅山隧道施工的全程监理,阐明了在施工中应重点注意的几个问题,在此基础上,总结出隧道工程质量监督中必须做到的几点,以确保隧道工程施工安全及工程质量.     

Evaluating Community Safety Programmes and Community Engagement: The Role of Qualitative Methods and Collaborative Approaches to Policy Research

This article explores the role of qualitative and collaborative methods when undertaking evaluation research into local community safety partnerships and neighbourhood strategies. It draws on recent examples of research and evaluation studies of regeneration and community safety programmes in a number of cities and towns in England. The discussion is set against the background of the major changes in policy relating to crime and community safety, particularly the introduction of Crime and Disorder Partnerships and their engagement with local communities. The value of qualitative methods in exploring some key questions is discussed, particularly within a collaborative approach to policy research.