A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

Coverability, Termination, and Finiteness in Recursive Petri Nets

In the early two-thousands, Recursive Petri nets have been introduced in order to model distributed planning of multi-agent systems for which counters and recursivity were necessary. Although Recursive Petri nets strictly extend Petri nets and context-free grammars, most of the usual problems (reachability, coverability, finiteness, boundedness and termination) were known to be solvable by using non-primitive recursive algorithms. For almost all other extended Petri nets models containing a stack, the complexity of coverability and termination are unknown or strictly larger than EXPSPACE. In contrast, we establish here that for Recursive Petri nets, the coverability, termination, boundedness and finiteness problems are EXPSPACE-complete as for Petri nets. From an expressiveness point of view, we show that coverability languages of Recursive Petri nets strictly include the union of coverability languages of Petri nets and context-free languages. Thus we get a more powerful model than Petri net for free.

Parallel Algorithm of Hierarchical Phrase Machine Translation Based on Distributed Network Memory

Machine translation has developed rapidly. But there are some problems in machine translation, such as good reading, unable to reflect the mood and context, and even some language machines can not recognize. In order to improve the quality of translation, this paper uses the SSCI method to improve the quality of translation. It is found that the translation quality of hierarchical phrases is significantly improved after using the parallel algorithm of machine translation, which is about 9% higher than before, and the problem of context free grammar is also solved. The research also found that the use of parallel algorithm can effectively reduce the network memory occupation, the original 10 character content, after using the parallel algorithm, only need to occupy 8 characters, the optimization reaches 20%. This means that the parallel algorithm of hierarchical phrase machine translation based on distributed network memory can play a very important role in machine translation.

Inflated 3D ConvNet context analysis for violence detection

According to the Wall Street Journal, one billion surveillance cameras will be deployed around the world by 2021. This amount of information can be hardly managed by humans. Using a Inflated 3D ConvNet as backbone, this paper introduces a novel automatic violence detection approach that outperforms state-of-the-art existing proposals. Most of those proposals consider a pre-processing step to only focus on some regions of interest in the scene, i.e., those actually containing a human subject. In this regard, this paper also reports the results of an extensive analysis on whether and how the context can affect or not the adopted classifier performance. The experiments show that context-free footage yields substantial deterioration of the classifier performance (2% to 5%) on publicly available datasets. However, they also demonstrate that performance stabilizes in context-free settings, no matter the level of context restriction applied. Finally, a cross-dataset experiment investigates the generalizability of results obtained in a single-collection experiment (same dataset used for training and testing) to cross-collection settings (different datasets used for training and testing).

Vector Symbolic Architectures for Context-Free Grammars

Vector symbolic architectures (VSA) are a viable approach for the hyperdimensional representation of symbolic data, such as documents, syntactic structures, or semantic frames. We present a rigorous mathematical framework for the representation of phrase structure trees and parse trees of context-free grammars (CFG) in Fock space, i.e. infinite-dimensional Hilbert space as being used in quantum field theory. We define a novel normal form for CFG by means of term algebras. Using a recently developed software toolbox, called FockBox, we construct Fock space representations for the trees built up by a CFG left-corner (LC) parser. We prove a universal representation theorem for CFG term algebras in Fock space and illustrate our findings through a low-dimensional principal component projection of the LC parser state. Our approach could leverage the development of VSA for explainable artificial intelligence (XAI) by means of hyperdimensional deep neural computation.

Good, Bad, and Ugly Leadership Patterns: Implications for Followers’ Work-Related and Context-Free Outcomes

This research responds to calls for a more integrative approach to leadership theory by identifying subpopulations of followers who share a common set of perceptions with respect to their leader's behaviors. Six commonly researched styles were investigated: abusive supervision, transformational leadership (TFL), contingent reward (CR), passive and active management-by-exception (MBE-P and MBE-A, respectively), and laissez faire/avoidant (LF/A). Study hypotheses were tested with data from four independent samples of working adults, three from followers ( N = 855) and a validation sample of leaders ( N = 505). Using latent profile analysis, three pattern cohorts emerged across all four samples. One subpopulation of followers exhibited a constructive pattern with higher scores on TFL and CR relative to other styles. Two cohorts exhibited destructive patterns, one where the passive styles of MBE-A, MBE- P and LF/A were high relative to the other styles (passive) and one where the passive styles co-occurred with abusive supervision (passive-abusive). Drawing on conservation of resources theory, we confirmed differential associations with work-related (i.e., burnout, vigor, perceived organizational support and affective organizational commitment) and context-free (i.e., physical health and psychological well-being) outcomes. The passive-abusive pattern was devastating for physical health, yet passiveness without abuse was damaging for psychological well-being. Interestingly, we find a clear demarcation between passiveness as “benign neglect” and passiveness as an intentional and deliberate form of leadership aimed at disrupting or undermining followers—hence, the two faces of passiveness: “bad” and “ugly.” We discuss the novel insights offered by a pattern (person)-oriented analytical strategy and the broader theoretical and practical implications for leadership research.