Estimating networks of sustainable development goals

An increasing number of researchers and practitioners advocate for a systemic understanding of the Sustainable Development Goals (SDGs) through interdependency networks. Ironically, the burgeoning network-estimation literature seems neglected by this community. We provide an introduction to the most suitable estimation methods for SDG networks. Building a dataset with 87 development indicators in four countries over 20 years, we perform a comparative study of these methods. We find important differences in the estimated network structures as well as in synergies and trade-offs between SDGs. Finally, we provide some guidelines on the potentials and limitations of estimating SDG networks for policy advice.     

Integration Framework and Key Technology of Big Earth Datafor SDGs and Beautiful China Evaluation

Construction of UN Sustainable Development Goals(SDGs) and Beautiful Chinashare the same meaning.Both of them endeavor to achieve national and regional social,environment and economy sustainable development.Accurate,reliable,timely and well classified data is the key for accurate evaluation of sustainable development.In order to address issues such as single data source,poor timeliness,lack of high accuracy and evaluation results unreliable,we puts forward the integration framework and standardization of the bigearth data which includes big network data,big remote sensing data,and big socioeconomic data facing to the evaluation of SDGs and Beautiful China.Then,the key technologies of network data acquisition and analysis,remote sensing data information intelligent extraction and socioeconomic data spatialization are analyzed from different perspectives.Taking the water contamination accident of SDG 6,forest information extraction of SDG 15,population spatialization of common requirements in SDGs as examples,the application of technological routes in supporting sustainable development evaluation based on big earth data are studied consequently.     

基于模糊决策过程的模糊计算树逻辑模型检测

针对由数据表述产生的不确定性模糊系统的模型检测问题,给出模糊计算树逻辑模型检测算法。首先,引入模糊决策过程作为此类系统的模型,其最大特点是在迁移过程中对动作的不确定性选择和状态表述的模糊性。然后,在模糊决策过程基础上,给出模糊计算树逻辑的语法和语义。最后,给出模糊计算树逻辑模型检测算法,该算法是将模糊计算树逻辑模型检测问题转换为模糊矩阵的合成运算,其优势是时间复杂度低、计算过程较为简洁。     

Formatting texts accessed randomly

Full-text systems that access text randomly cannot normally determine the format operations in effect for a given target location. The problem can be solved by viewing the format marks as the non-terminals in a format grammar. A formatted text can then be parsed using the grammar to build a data structure that serves both as a parse tree and as a search tree. While processing a retrieved segment, a full-text system can follow the search tree from root to leaf, collecting the format marks encountered at each node to derive the sequence of commands active for that segment. The approach also supports the notion of a ‘well formatted’ document and provides a means for verifying the well-formedness of a given text. To illustrate the approach, a sample set of format marks and a sample grammar are given suitable for formatting and parsing the article as a sample text.     

处理图像语言的格点自动机与格点文法

Giammarresi与Restivo在一篇综述中总结出一个关于可识别的图像语言(即2维矩形语言)REC的等价性定理.对比1维字语言的相应结果,其中还缺少关于生成文法的相应一环.提出了一种(矩形的)格点文法,正好弥补了这一缺环.而取代2维on-line tesselation自动机,引入了格点自动机的概念.一方面,它与经典的2元树型自动机更相似,另一方面,它也是格点文法与等价性定理中关于REC的其他描述方式之间的一座桥梁.同时,标准的existential monadic二阶逻辑也被一种更弱的规范框架——positive monadic分划逻辑所取代.由此导出一个新的更完整的关于REC的等价性定理.     

A Bi‐Directional Procedural Model for Architectural Design

It is a challenge for shape grammars to incorporate spatial hierarchy and interior connectivity of buildings in early design stages. To resolve this difficulty, we developed a bi‐directional procedural model: the forward process constructs the derivation tree with production rules, while the backward process realizes the tree with shapes in a stepwise manner (from leaves to the root). Each inverse‐derivation step involves essential geometric‐topological reasoning. With this bi‐directional framework, design constraints and objectives are encoded in the grammar‐shape translation. We conducted two applications. The first employs geometric primitives as terminals and the other uses previous designs as terminals. Both approaches lead to consistent interior connectivity and a rich spatial hierarchy. The results imply that bespoke geometric‐topological processing helps shape grammar to create plausible, novel compositions. Our model is more productive than hand‐coded shape grammars, while it is less computation‐intensive than evolutionary treatment of shape grammars.     

Direct automated inversion of logic grammars

Reversibility of logic grammars in natural language processing is desirable for both theoretical and practical reasons. This paper addresses this topic in describing a new approach to automated inversion of logic grammars: the Direct Inversion Approach (dia). A logic grammar is inverted by automatically altering the order of literals in the grammar and reformulating certain recursive procedures at compile time. The inversion process results in a new executable grammar, which is evaluated top-down and left-to-right (using a standard Prolog interpreter), but not left-to-right with respect to the original grammar. Thedia improves upon related approaches not only in being fully automated and computationally tractable, but also with respect to the class of grammars it is able to invert and the performance of the new executable grammar produced. The presented research was sponsored by Teilprojekt B4 “Constraints on Grammar for Efficient Generation” of the Sonderforschungsbereich 340 of the Deutsche Forschungsgemeinschaft.     

STOCHASTIC PARSING AND EVOLUTIONARY ALGORITHMS

This article aims to show the effectiveness of evolutionary algorithms in automatically parsing sentences of real texts. Parsing methods based on complete search techniques are limited by the exponential increase of the size of the search space with the size of the grammar and the length of the sentences to be parsed. Approximated methods, such as evolutionary algorithms, can provide approximate results, adequate to deal with the indeterminism that ambiguity introduces in natural language processing. This work investigates different alternatives to implement an evolutionary bottom-up parser. Different genetic operators have been considered and evaluated. We focus on statistical parsing models to establish preferences among different parses. It is not our aim to propose a new statistical model for parsing but a new algorithm to perform the parsing once the model has been defined. The training data are extracted from syntactically annotated corpora (treebanks) which provide sets of lexical and syntactic tags as well as the grammar in which the parsing is based. We have tested the system with two corpora: Susanne and Penn Treebank, obtaining very encouraging results.     

Deciding Regular Grammar Logics with Converse Through First-Order Logic

We provide a simple translation of the satisfiability problem for regular grammar logics with converse into GF², which is the intersection of the guarded fragment and the 2-variable fragment of first-order logic. The translation is theoretically interesting because it translates modal logics with certain frame conditions into first-order logic, without explicitly expressing the frame conditions. It is practically relevant because it makes it possible to use a decision procedure for the guarded fragment in order to decide regular grammar logics with converse. The class of regular grammar logics includes numerous logics from various application domains. A consequence of the translation is that the general satisfiability problem for every regular grammar logics with converse is in EXPTIME. This extends a previous result of the first author for grammar logics without converse. Other logics that can be translated into GF² include nominal tense logics and intuitionistic logic. In our view, the results in this paper show that the natural first-order fragment corresponding to regular grammar logics is simply GF² without extra machinery such as fixed-point operators.     

The Displacement Calculus

If all dependent expressions were adjacent some variety of immediate constituent analysis would suffice for grammar, but syntactic and semantic mismatches are characteristic of natural language; indeed this is a, or the, central problem in grammar. Logical categorial grammar reduces grammar to logic: an expression is well-formed if and only if an associated sequent is a theorem of a categorial logic. The paradigmatic categorial logic is the Lambek calculus, but being a logic of concatenation the Lambek calculus can only capture discontinuous dependencies when they are peripheral. In this paper we present the displacement calculus, which is a logic of intercalation as well as concatenation and which subsumes the Lambek calculus. On the empirical side, we apply the new calculus to discontinuous idioms, quantification, VP ellipsis, medial extraction, pied-piping, appositive relativisation, parentheticals, gapping, comparative subdeletion, cross-serial dependencies, reflexivization, anaphora, dative alternation, and particle shift. On the technical side, we prove that the calculus enjoys Cut-elimination.     

概率SDG模型及故障分析推理方法

符号有向图（SDG）是用来表示大规模复杂系统中变量之间因果影响关系的一种重要工具,但其存在一些不易克服的缺点.为此,首先提出一种新的模型--概率SDG模型,用条件概率描述故障之间的传递关系;然后在概率SDG模型的框架下,提出一种故障分析诊断的推理方法,即利用图消去算法和连接树算法进行贝叶斯推理,并计算出故障概率.最后以65 t/h锅炉系统为例,研究建立其概率SDG模型,并在此基础上验证了上述模型和推理方法的有效性.     

Reversible logic grammars for natural language parsing and generation

The use of a single grammar in natural language parsing and generation is most desirable for a variety of reasons, including efficiency, perspicuity, integrity, robustness, and a certain amount of elegance. These characteristics have been noted before by several researchers, but it was only recently that more serious attention started to be paid to the problem of creating a bidirectional system for natural language processing. In this paper we discuss a somewhat more radical version of the problem: given a parser for a language, can we reverse it so that it becomes an efficient generator for the same language? Furthermore, since both the parser and the generator are based upon the same grammar, are there any normalization conditions upon the form of the grammar that must be met in order to assure the maximum efficiency of the reversed program? Can other grammars be transformed into the normal form? We describe the results of an experiment with PROLOG-based logic grammar which has been derived from a substantial-coverage string grammar for English. We present an alogorithm for automated inversion of a unification parser into an efficient unification generator, using the collections of minimal sets of essential arguments for predicates. We discuss the scope of the present version of the algorithm and then point out several possible avenues for extension. We also outline a preliminary solution to the question of grammar's “normal form” and suggest a handful of normalizing transformations that can be used to enhance the efficiency of the generator. This research interacts closely with a Japanese-English machine translation project at New York University, for which the first implementation of the inversion algorithm has been prepared.     

The Grammar Deployment Kit — System Demonstration —

Grammar deployment is the process of turning a given grammar specification into a working parser. The Grammar Deployment Kit (for short, GDK) provides tool support in this process based on grammar engineering methods. We are mainly interested in the deployment of grammars for software renovation tools, that is, tools for software re- and reverse engineering. The current version of GDK is optimized for Cobol. We assume that grammar deployment starts from an initial grammar specification which is maybe still ambiguous or even incomplete. In practice, grammar deployment binds unaffordable human resources because of the unavailability of suitable grammar specifications, the diversity of parsing technology as well as the limitations of the technology, integration problems regarding the development of software renovation functionality, and the lack of tools and adherence to firm methods for grammar engineering. GDK helps to largely automate grammar deployment because tool support for grammar adaptation and parser generation is provided. We support different parsing technologies, among them btyacc, that is, yacc with backtracking. GDK is free software.     

A characterization of attribute evaluation in passes

Summary This paper describes the evaluation of semantic attributes in a bounded number of passes from left-to-right and/or from right-to-left over the derivation tree of a program. Evaluation strategies where different instances of the same attribute in any derivation tree are restricted to be evaluated in one pass, with for every derivation tree the same pass number, are referred to as simple multi-pass whereas the unrestricted pass-oriented strategies are referred to as pure multi-pass.A graph theoretic characterization is given, showing in which cases an attribute grammar meets the simple multi-pass requirements and what are the minimal pass numbers of its attributes for a given sequence of pass directions. For the special cases where only left-to-right passes are made or where left-to-right and right-to-left passes strictly alternate, new algorithms are developed that associate minimal pass numbers with attributes and indicate in case of failure the attributes that cause the rejection of the grammar. Mixing of a simple multi-pass strategy with other evaluation strategies, in case the grammar is not simple multi-pass, is discussed.     

Mining Layered Grammar Rules for Action Recognition

We propose a layered-grammar model to represent actions. Using this model, an action is represented by a set of grammar rules. The bottom layer of an action instance’s parse tree contains action primitives such as spatiotemporal (ST) interest points. At each layer above, we iteratively mine grammar rules and “super rules” that account for the high-order compositional feature structures. The grammar rules are categorized into three classes according to three different ST-relations of their action components, namely the strong relation, weak relation and stochastic relation. These ST-relations characterize different action styles (degree of stiffness), and they are pursued in terms of grammar rules for the purpose of action recognition. By adopting the Emerging Pattern (EP) mining algorithm for relation pursuit, the learned production rules are statistically significant and discriminative. Using the learned rules, the parse tree of an action video is constructed by combining a bottom-up rule detection step and a top-down ambiguous rule pruning step. An action instance is recognized based on the discriminative configurations generated by the production rules of its parse tree. Experiments confirm that by incorporating the high-order feature statistics, the proposed method largely improves the recognition performance over the bag-of-words models.     

Predicate Logic Based Image Grammars for Complex Pattern Recognition

Predicate logic based reasoning approaches provide a means of formally specifying domain knowledge and manipulating symbolic information to explicitly reason about different concepts of interest. Extension of traditional binary predicate logics with the bilattice formalism permits the handling of uncertainty in reasoning, thereby facilitating their application to computer vision problems. In this paper, we propose using first order predicate logics, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest. Detections from low level feature detectors are treated as logical facts and, in conjunction with logical rules, used to drive the reasoning. Positive and negative information from different sources, as well as uncertainties from detections, are integrated within the bilattice framework. We show that this approach can also generate proofs or justifications (in the form of parse trees) for each hypothesis it proposes thus permitting direct analysis of the final solution in linguistic form. Automated logical rule weight learning is an important aspect of the application of such systems in the computer vision domain. We propose a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, interprets rule uncertainties as link weights in the network, and applies a constrained, back-propagation algorithm to converge upon a set of rule weights that give optimal performance within the bilattice framework. Finally, we evaluate the proposed predicate logic based pattern grammar formulation via application to the problems of (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures as viewed in satellite imagery. We also evaluate the optimization approach on real as well as simulated data and show favorable results.     

基于模型的Fuzzing测试脚本自动化生成

针对基于知识的Fuzzing测试技术存在脚本编写工作量大的问题,提出一种基于模型的Fuzzing测试脚本自 动生成方法。方法首先以高阶属性文法形式化地描述数据模型,获取统一的、与测试环境无关的数据格式描述;然后 依据文法模型,将样本解析为带格式知识的文法分析树;最后建立文法分析树与测试逻辑的关联关系,实现自动化的 测试脚本生成。实验结果表明,所提出的方法能够自动生成有效的测试脚本,并发现软件中潜在的安全漏洞。