Automatic analysis of textual garden path phenomenon： A computational perspective

This paper puts forward a text-circled semantic schema by which a special flow chart of cognitive alteration and processing breakdown in Machine Translation （MT） system is clearly presented. Based on the theoretical analysis of textual Garden Path Phenomenon （GPP）, we devise a formula to measure the dramatically changeable value of textual GPP. The data-provided evidence in A Farewell to Arms shows the textual GPP can motivate the development of plots and adjust the analyst＇s original horizon of expectation. Despite the limitation of incompatible, subjective and sample-restricted features involved in the theoretical framework and formula, this computational analysis makes MT system pay more attention to text-circled cognitive alteration rather than only highlight the lexical or syntactic translation, and as a result aims to make the effectiveness of machine translation of the literary text improved.     

Preface

Natural Language Processing(NLP) is a field of computer science and linguistics concerning the interactions between computers and human(natural) languages.There have been sufficient successes in the past decades in this area,which suggest that NLP is now and will continue to be a major area of computer sciences and information technologies. The goal of this special section is to present high-quality contributions that explicate reasoning involved in different areas of NLP both at theoretical and practical levels.The special section has received enthusiastic     

Chinese-based research on subject-covered information retrieval supervised by textual semantic domain

By means of analysis of artificial intervention in ready-retrieved text, training set used to compare with new texts from large-scale real texts corpus is provided. It is based on the data-originated presentation of training set that a special formula to calculate semantic cohesion between new texts and training set is devised. The semantic cohesion of new text is the average value of semantic evaluation of all elements involved, and semantic evaluation of an element depends on its semantic relevance with the training set and on the semantic ratio of its domain to synonymous domain. In terms of empirical verification a conclusion is drawn that semantic cohesion is the key measurement standard of textual retrieval. Despite the advantages of textual retrieval, limitations of formula-raised condition and analyst＇s accomplishments make the analysis involved in this paper imperfect.     

Generation and Control of Game Virtual Environment

In this paper,we present a framework for the generation and control of an Internet-based 3-dimensionai game virtual environment that allows a character to navigate through the environment.Our framework includes 3-dimensional terrain mesh data processing,a map editor,scene processing,collision processing,and waikthrough control.We also define an environment-specific semantic information editor,which can be applied using specific location obtained from the real world.Users can insert text information related to the characters real position in the real world during navigation in the game virtual environment.     

Alignment of the Polish-English Parallel Text for a Statistical Machine ＂Translation

Text alignment is crucial to the accuracy of MT （Machine Translation） systems, some NLP （Natural Language Processing） tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish （not position-sensitive language） to English experiments. This alignment approach was developed on the TED （Translanguage English Database） talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with the described tool is shown.     

Path selection in disaster response management based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.     

An Approach to Intelligent Speech Production System

In the paper an intelligent speech production system is established by using language information processing technology.The concept of bi-directional grammar is proposed in Chinese language information processing and a corresponding Chinese characteristic network is completed.Correct text can be generated through grammar parsing and some additional rules.According to the generated text the system generates speech which has good quality in naturalness and intelligibility using Chinese Text-to-Speech Conversion System.     

Dynamic Fractal Transform with Applications to Image Data Compression

A recent trend in computer graphics and image processing is to use Iterated Function System(IFS)to generate and describe both man-made graphics and natural images.Jacquin was the first to propose a fully automation gray scale image compression algorithm which is referred to as a typical static fractal transform based algorithm in this paper.By using this algorithm,an image can be condensely described as a fractal transform operator which is the combination of a set of reactal mappings.When the fractal transform operator is iteratedly applied to any initial image,a unique attractro(reconstructed image)can be achieved.In this paper,a dynamic fractal transform is presented which is a modification of the static transform.Instea of being fixed,the dynamic transform operator varies in each decoder iteration,thus differs from static transform operators.The new transform has advantages in improving coding efficiency and shows better convergence for the deocder.     

A memristor-based architecture combining memory and image processing

Image processing is a type of memory-access-intensive application and is applied in many fields.Logic operations are very simple ones in image processing.During these operations,memory access takes a majority of the total time consumed,which puts a great pressure on memory access speed and bandwidth.However,in traditional von Neumann architecture,memory access is the inherent bottleneck of the system;that is,the speed of memory’s data supply is far lower than the data request of processor.Memristor is considered to be the fourth circuit element after resistor,capacitor and inductor.It has the capacity of both processing and memory,which supplies a new idea for solving the"memory wall"problem.In this paper,memristor is used to build an architecture combining computing and memory,where the memory has the ability to handle some simple image processing operations.This architecture can reduce readings and writings of memory effectively,which saves memory bandwidth thus improving the efficiency of the system.Logic operations of images are considered in this paper to validate the architecture.The experimental results and theoretical analysis indicate that the architecture can reduce memory access effectively.     

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.     

An Ontology-Based Approach for Semantic Conflict Resolution in Database Integration

An important task in database integration is to resolve data conflicts, on both schema-level and semantic-level. Especially difficult the latter is. Some existing ontology-based approaches have been criticized for their lack of domain generality and semantic richness. With the aim to overcome these limitations, this paper introduces a systematic approach for detecting and resolving various semantic conflicts in heterogeneous databases, which includes two important parts： a semantic conflict representation model based on our classification framework of semantic conflicts, and a methodology for detecting and resolving semantic conflicts based on this model. The system has been developed, experimental evaluations on which indicate that this approach can resolve much of the semantic conflicts effectively, and keep independent of domains and integration patterns.     

Event semantic recognizing based on Markov chain

Owing to the enormous information and complex structure, video semantic processing is a tricky issue all along. Current researches are restricted within recognizing relative simple semantic in some certain domains. This paper brings forward a novel method transiting low-level feature to high-level semantic with Markov chain by stages, which takes object semantic as the core. This method is valid for recognizing complex event semantic. Semantic concept mapping mechanism based on semantic template is presented to realize the automatic recognition of video semantic. In the experiment contrasting with IBM＇s IMAT, our method shows more extensive recognition range and higher accuracy. Experimental results are encouraging, and indicate that the performance of the proposed approach is effective.     

从UML顺序图生成状态图的一个方法

UML (Unified Modeling Language) is a visual modeling language used for specifying,visualizing,constructing,and documenting the artifacts of software systems by various diagrams.It has been widely accepted as a standard modeling language in both academic and industrial areas.UML sequence diagrams are mostly used in specifying system requirements.By representing interactions,which are arranged in time sequence,between the objects in a system,sequence diagrams can construct scenarios indicating the system‘‘s functions.A UML statechart diagram is a graph shows the sequences of states that an object or an interaction goes through during its life in response to received stimuli,together with its responses and actions.It‘‘s useful in the design stage of system development.This essay discusses the computer-aided transformation from sequence diagrams to statechart diagrams,which can offer strong support for the transfering from requirement analysis to system design in the software development process.With OCL (Object Control Language) semantic constrain,a transform algorithm is provided in the paper.And the differences with the related works are also mentioned.     

Method for reducing cloud workflow completion time under the task interruption

As more and more large-scale scientific workflows are delivered to clouds,the business model of workflow-as-a-service is emerging.But there are many kinds of threats in the cloud environment,which can interrupt the task execution and extend the workflow completion time.As an important QoS parameter,the workflow completion time is determined by the critical task path.Therefore,critical path redundancy method is proposed to create a redundant path having the interact parallel relationship with the critical path,which can provide the protection for the tasks in the critical path and reduce the probability of the critical path interruption.Computing instance allocation is an essential part of the cloud workflow execution,since only the tasks assigned the instance can begin execution.In order to further reduce the workflow completion time,computing instance allocation algorithm based on HEFT (heterogeneous earliest finish time) is proposed.The algorithm considers diverse task dependency relationships and takes full advantages of the critical path redundancy method,which can improve the efficiency of workflow execution.Experimental results demonstrate that the proposed method can effectively reduce the cloud workflow completion time under the task interruption.     

The multiple-parameter fractional Fourier transform

The fractional Fourier transform （FRFT） has multiplicity, which is intrinsic in fractional operator. A new source for the multiplicity of the weight-type fractional Fourier transform （WFRFT） is proposed, which can generalize the weight coefficients of WFRFT to contain two vector parameters m,n ∈ Z^M . Therefore a generalized fractional Fourier transform can be defined, which is denoted by the multiple-parameter fractional Fourier transform （MPFRFT）. It enlarges the multiplicity of the FRFT, which not only includes the conventional FRFT and general multi-fractional Fourier transform as special cases, but also introduces new fractional Fourier transforms. It provides a unified framework for the FRFT, and the method is also available for fractionalizing other linear operators. In addition, numerical simulations of the MPFRFT on the Hermite-Gaussian and rectangular functions have been performed as a simple application of MPFRFT to signal processing.     

Integration of process planning and production scheduling based on genetic algorithm

Lacking of flexibility in the traditional workshop production, a genetic algorithm is proposed to implement the integration of process planning and production scheduling. In this paper, the processing routes and processing machine are selected through chromosome crossover and mutation, in order to implement the optimal scheduling of the flexible workshop production. Meanwhile, a performance test about the integration of process planning and production scheduling is implemented, and the results shows that the genetic algorithm is efficient to obtain optimal or near optimal process routes which can meet the requirements of production scheduling.     

Ganglion-Based Balance Design of Multi-Layer Model and Its War chfulness-Keeping

An important problem of machine vision is the balance among the efficiency, accuracy and computation cost.The visual system of man can keep watchfulness to the perimeter of a visual field and subtly process information emerging in the center of the visual field at the same time. This kind of requirement assignment of computation can virtually ease the demand of hardware both in quantity and complexity. Therefore designing an artificial model based on biological mechanism is an effective approach. In this paper a multi-layer neural model is designed based on the multi-scale receptive fields of ganglions in retina. The model can keep watch on the periphery part of a scene while processing the center information of the scene. And why it can balance the hardware complexity, processing precision and computational intensity is analyzed.An experiment is done to test the model‘s sensitivity in watchfulness keeping and its efficiency and veracity in environment sampling. This model may provide valuable inspiration in the implementation of real-time processing and the avoidance of expensive computation cost in machine vision.     

Semantic Computation in a Chinese Question-Answering System

This paper introduces a kind of semantic computation and presents how to combine it into our Chinese Question-Answering (QA) system. Based on two kinds of language resources, Hownet and Cilin, we present an approach to computing the similarity and relevancy between words. Using these results, we can calculate the relevancy between two sentences and then get the optimal answer for the query in the system. The calculation adopts quantitative methods and can be incorporated into QA systems easily, avoiding some difficulties in conventional NLP (Natural Language Processing) problems. The experiments show that the results are satisfactory.     

SVM-based ontology matching approach

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.     

GDM: A New Graph Based Data Model Using Functional Abstractionx

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph. Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.