P&T-Inf: A Result Inference Method for Context-Sensitive Tasks in Crowdsourcing

Context-Sensitive Task(CST)is a complex task type in crowdsourc-ing,such as handwriting recognition,route plan,and audio transcription.The current result inference algorithms can perform well in simple crowd-sourcing tasks,but cannot obtain high-quality inference results for CSTs.The conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing,but this method ignores the context correlation among subtasks and reduces the quality of result inference.To solve this problem,we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer(P&T-Inf)for CSTs.Firstly,we screen the candidate results of context-sensitive tasks based on the partially ordered set.If there are parallel candidate sets,the conditional mutual information among subtasks containing context infor-mation in external knowledge(such as Google n-gram corpus,American Contemporary English corpus,etc.)will be calculated.Combined with the tree augmented naive(TAN)Bayes model,the maximum weighted spanning tree is used to model the dependencies among subtasks in each CST.We collect two crowdsourcing datasets of handwriting recognition tasks and audio transcription tasks from the real crowdsourcing platform.The experimental results show that our approach improves the quality of result inference in CSTs and reduces the time cost compared with the latest methods.

Interrelation of Languages of Colored Petri Nets and Some Traditional Languages

The article studies the interrelation of Languages of Colored Petri Nets and Traditional formal languages. The author constructed the graph of Colored Petri Net, which generates L* Context-free language. This language may not be modeled using standard Petri Nets [1]. The Venn graph and diagram that the author modified [1], show the interrelation between languages of Colored Petri Nets and some Traditional languages. Thus the class of languages of Colored Petri Nets is supposed to include an entire class of Context-free languages.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Forest Grammar (Ⅱ)

The syntactic parsing algorithm of weak precedence forest grammar has been introduced and the correctness and unambiguity of this algorithm have been proved. An example is given to the syntactic parsing procedure of weak precedence forest grammar.

Forest Grammar(Ⅰ)

Forest grammar,a new type of high-dimensional grammar,is proposed in this paper,of which both the left and the right parts of every production are concatenations of tree structures.A classification of forest grammar is studied,especially,a subclass of the forest grammar,i.e.the context-sensitive forest grammar,and one of its subclasses is defined,called the weak precedence forest grammar.

Weak Precedence Story Parsing Grammar

Story understanding is one of the important branches of natural languageunderstanding research in AI techlliques. The story understanding approachbased on Story Parsing Grammar (SPG) involves that SPG is used to rep-resent different abstracting processes of stories with different levels in storyunderstanding and that the story understanding process is converted to therecognition process of stories using the syntactic parser of SPG. This kind ofstory understanding is cal1ed story parsing. In this paper, firstly a subclassof SPG, called Weak Precedence SPG (WPSPG), is defined. Afterwards thesyntactic parsing algorithm of WPSPG is studied. An example of story parsingis also given.

Story Parsing Grammar

Story understanding is one of the important branches of natural language under-standing research in AI techniques. A new approach to story understanding is proposedin this paper. The so-called Story Parsing Grammar (SPG) is used to represent thestory abstracting processes with different degrees in story understanding, and the storyunderstanding process is converted to the story recognizing process done by the syn-tactic parser of SPG. This kind of story understanding is called story parsing. In thispaper, firstly, a survey of story understanding research is given. Secondly, by the clas-sification of various kinds of story structures, the so-called Case Frame Forest (CFF) isproposed to represent the superficial meaning of story. Based on CFF, a high- dimen-sional grammar, called Forest Grammar (FG), is defined. Furthermore, SPG is definedas a subclass of context-sensitive FG. Considering the context-sensitivity of story con-tent, a type of context-sensitive derivation is defined in the definition of SPG. Lastly,data about runtime efficiency of the syntactic parsing algorithm of weak precedenceSPG, a subclass of SPG, are given and analysed.

A constraint-weaving approach to points-to analysis for AspectJ

Points-to analysis is a static code analysis tech- nique that establishes the relationships between variables of references and allocated objects. A number of points-to analysis algorithms have been proposed for procedural and object-oriented languages like C and Java, while few of them can be used for AspectJ as we know so far. One main rea- son is that Aspect/is an aspect-oriented language which im- plements the separation of crosscutting concerns by advices, pointcuts, and inter-type declarations, while a points-to anal- ysis of AspectJ programs may be imprecise because any as- pect woven into the base code may change the points-to rela- tions in the program and thus a conservative analysis has to be taken in order to handle the aspects. In this paper, we pro- pose a context-sensitive points-to analysis technique called AJPoints for AspectJ. Similar to the weaving mechanism for AspectJ, AJPoints obtains the constraints and templates on the points-to relations for the base code and the aspects, re- spectively, but weaves and solves them in an iterative manner in order to cross the boundary between the base code and the aspects. We have implemented AJPoints on abc AspectJ compiler and evaluated it by using twelve AspectJ benchmark programs. The experimental can achieve a high precision pectJ programs. results show that our technique about points-to relations in As-