The function-level code generation is an important task in the combination of software engineering and artificial intelligence, which aims to improve the productivity of software development by automatically generating function-level code based on task descriptions. However, this task currently suffers from several problems: 1) in the fine-tuning phase, current large language models cannot sufficiently capture the detailed syntactic structure of the code dataset; 2) previous retrieval-augmented methods cannot adequately consider the complex dependencies between code snippets in external code repositories; 3) existing large language models introducing self-repair mechanism tend to overfocus on previously generated erroneous code during the self-repair process. To solve these problems, we propose a retrieval filtering and thought enhancement framework for function-level code generation based on large language model. In our model, we design an abstract syntax tree mapping preprocessing module to preprocess the dataset and help large language models learn the detailed syntax information of the dataset. Furthermore, we design a retrieval filtering and thought enhancement module to retrieve the most relevant snippets of code for the task and to enhance the chain-of-thought of our model. In addition, we design a self-repair mechanism to prevent large language models from overfocusing on generated erroneous code, helping them explore more solutions to repair the erroneous code. We experimented and evaluated our model on HumanEval, MBPP, and MultiPLE benchmarks to compare with other baseline models.

Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigation of spatial and temporal evolutions of phenomena in many application domains. Recent research literature has explored the sequential patterns on transaction data and trajectory analysis on moving objects. However, these methods cannot be directly applied to mining sequential patterns from a large number of spatio-temporal events. Two major research challenges still remain: 1) the definition of significance measures for spatio-temporal sequential patterns to avoid spurious ones and 2) the algorithmic design under the significance measures, which may not guarantee the downward closure property. In this paper, we propose a sequence index as the significance measure for spatio-temporal sequential patterns, which is meaningful due to its interpretability using spatial statistics. We propose a novel algorithm called Slicing-STS-miner to tackle the algorithmic design challenge using the spatial sequence index, which does not preserve the downward closure property. We compare the proposed algorithm with a simple algorithm called STS-miner that utilizes the weak monotone property of the sequence index. Performance evaluations using both synthetic and real-world data sets show that the slicing-STS-miner is an order of magnitude faster than STS-Miner for large data sets.

We present a novel framework for organizing large collections of images in a hierarchical way, based on scene semantics. Rather than score images directly, we use them to score the scene in order to identify typical views and important locations which we term Geo-Relevance. This is done by relating each image with its viewing frustum which can be readily computed for huge collections of images nowadays. The frustum contains much more information than only camera position that has been used so far. For example, it distinguishes between a photo of the Eiffel Tower and a photo of a garbage bin taken from the exact same place. The proposed framework enables a summarized display of the information and facilitates efficient browsing.

Given a query point and a collection of spatial features, a multi-type nearest neighbor query finds the shortest tour for the query point in a way such that only one instance of each feature type is visited during the tour. For example, a tourist may be interested in finding the shortest tour which starts at a hotel and passes through a post office, a gas station, and a grocery store. The multi-type nearest query problem is different from the traditional nearest neighbor query problem, since there are many objects for each feature type and the shortest tour should pass through only one object from each feature type. In this paper, we propose R-tree based optimal solutions, which exploit a page-level upper bound for efficient computation. Also, since this problem is a generalized Traveling Salesman Problem (TSP) and is NP-hard, we provide several heuristic methods for the case that there are a large number of feature types in the data. Finally, experimental results are provided to show the strength of the proposed algorithms and design decisions related to performance tuning.

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping co-location mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined co-location patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using clustering techniques. First, we propose a novel framework for co-location mining using clustering techniques. We show that the proximity of two spatial features can be captured by summarizing their spatial objects embedded in a continuous space via various techniques. We define the desired properties of proximity functions compared to similarity functions in clustering. Furthermore, we summarize the properties of a list of popular spatial statistical measures as the proximity functions. Finally, we show that clustering techniques can be applied to reveal the rich structure formed by co-located spatial features. A case study on real datasets shows that our method is effective for mining co-locations from large spatial datasets

Given a large spatio-temporal database of events, where each event consists of the following fields: event-ID, time, location, event-type, mining spatio-temporal sequential patterns is to identify significant event type sequences. Such spatiotemporal sequential patterns are crucial to investigate spatial and temporal evolutions of phenomena in many application domains. In this paper, we propose a sequence index as the significance measure for spatio-temporal sequential patterns, which is meaningful due to its interpretability using spatial statistics. We propose two algorithms, namely STSMiner and Slicing-STS-Miner, to tackle the algorithmic design challenges under the spatial sequence index which does not preserve the downward closure property. We evaluate the algorithms by experimentally conducting performance evaluations using both synthetic and real world datasets.

Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety, and data mining algorithms are needed to filter out large uninteresting parts of spatial datasets. We construct a web-based visualization software package for observing the summarization of spatial patterns and temporal trends. We also present data mining algorithms for filtering out vast parts of datasets for spatial outlier patterns. The algorithms were implemented and tested with a real-world set of Minneapolis-St. Paul (Twin Cities) traffic data.

Identification of outliers can lead to the discovery of unexpected, interesting, and useful knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define statistical tests, analyze the statistical foundation underlying our approach, design several fast algorithms to detect spatial outliers, and provide a cost model for outlier detection procedures. In addition, we provide experimental results from the application of our algorithms on a Minneapolis-St.Paul(Twin Cities) traffic dataset to show their effectiveness and usefulness.