Ask Me Any Type:Type Inference Plugin for Partial Code on the Web and in the Integrated Development Environment

Inferring the fully qualified names(FQNs)of undeclared receiving objects and non-fully-qualified type names(non-FQNs)in partial code is critical for effectively searching,understanding,and reusing partial code.Existing type inference tools,such as COSTER and SNR,rely on a symbolic knowledge base and adopt a dictionary-lookup strategy to map simple names of undeclared receiving objects and non-FQNs to FQNs.However,building a symbolic knowledge base requires parsing compilable code files,which limits the collection of APIs and code contexts,resulting in out-of-vocabulary(OOV)failures.To overcome the limitations of a symbolic knowledge base for FQN inference,we implemented Ask Me Any Type(AMAT),a type of inference plugin embedded in web browsers and integrated development environment(IDE).Unlike the dictionary-lookup strategy,AMAT uses a cloze-style fill-in-the-blank strategy for type inference.By treating code as text,AMAT leverages a fine-tuned large language model(LLM)as a neural knowledge base,thereby preventing the need for code compilation.Experimental results show that AMAT outperforms state-of-the-art tools such as COSTER and SNR.In practice,developers can directly reuse partial code by inferring the FQNs of unresolved type names in real time.

Type System in Programming Languages

Type system provides a precise description of a programming language. This is a prerequisite for the implementation and use of language. It also conducts mechanical and transparent type-checking on programs to prevent the occurrence of execution error during the running of programs. So, it can be said that, on the one hand, type system works as a formal tool to do mathematical analysis of language; on the other hand, it is a formal method for rigorously and precisely designing and implementing language. In this paper, some basic concepts of type system are discussed first. And then, the implementation of a graph- rewriting- based functional language - SClean's type system is given in details. It is hoped that the proposed method of using and implementing type system is of practical usefulness.

The Proportional Hazards Model for Multiple Type Recurrent Gap Times

Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covaxiates on the censored event processes of interest.An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions.We examine asymptotic properties of the proposed estimators.Finite sample properties of these estimators are demonstrated by simulations.

XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application

Knowledge bases(KBs)are often greatly incomplete,necessitating a demand for KB completion.Although XLORE is an English-Chinese bilingual knowledge graph,there are only 423,974 cross-lingual links between English instances and Chinese instances.We present XLORE2,an extension of the XLORE that is built automatically from Wikipedia,Baidu Baike and Hudong Baike.We add more facts by making cross-lingual knowledge linking,cross-lingual property matching and fine-grained type inference.We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.

Image restoration techniques based on fuzzy neural networks

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.