Text Clustering Algorithm Based on the Graph Structures of Semantic Word Co-occurrence

Chang, Jin; Bai, Qiuchan

doi:10.1109/isai.2016.0112

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Our experimental results verify the reasonableness and effectiveness of the proposed algorithm as a theoretical basis for practical software crowdsourcing task allocation. In a follow-up study currently under way, the KMeans clustering algorithm [28], [29] is used to calculate the similarity between tasks, identify the historical task with the highest similarity to the task to be assigned. The complexity of the historical task will be used as the estimated complexity of the task to be assigned.…”

Section: Discussionmentioning

confidence: 99%

Software Crowdsourcing Task Allocation Algorithm Based on Dynamic Utility

Wang

Zhou

2019

IEEE Access

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For crowdsourcing software development, poor matching between the workers' development skills and the requirements for the assigned tasks results in poor quality task completion. In this paper, we propose a dynamic utility task allocation algorithm (DUTA), a software crowdsourcing task allocation algorithm based on the dynamic utility. First, using the attributes provided by the worker registration information, we estimate the initial value of a worker's development abilities based on the attribute weights and levels. Second, the worker's development capabilities are calculated based on his or her history of the completed tasks, including task complexity, quality, and development efficiency. The worker's record of development capability is updated dynamically. Then, based on the skill weights, we calculate the degree to which the task requirements matche the worker's skills. Finally, we take the product of the worker's development ability and the degree of skill matching as the allocation utility, and the total utility is maximized as the optimization goal. To solve the optimal match between tasks and workers, we use the Kuhn-Munkres algorithm with a weighted bipartite graph. Our experimental results showed that DUTA provided good performance in total allocation utility, with a better task allocation success rate than the algorithm based on the user reliability. The DUTA achieved an average allocation accuracy of up to 85.63%, demonstrating that it can guide task assignments effectively.

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Section: Discussionmentioning

confidence: 99%

Software Crowdsourcing Task Allocation Algorithm Based on Dynamic Utility

Wang

Zhou

2019

IEEE Access

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“…We start with event clustering graphs, for which Jin and Bai (2016) proposed a long document clustering approach utilizing a directed GoW for representing the word features contained in each document. The document clusters were generated based on the maximum common subgraphs between each document graph.…”

Section: Related Workmentioning

confidence: 99%

Tracing Topic Transitions with Temporal Graph Clusters

Jing

Zhang

et al. 2021

FLAIRS

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Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.

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“…The semantic text clustering methods based on graph structure has been researched and developed in depth for decades [56,[62][63][64]. In the graph structure, the weight-graph is considered as the most common way of representing a correlation in text content.…”

Section: Graph Sturcturementioning

confidence: 99%

Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Yuan

et al. 2021

Electronics

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Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.

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Text Clustering Algorithm Based on the Graph Structures of Semantic Word Co-occurrence

Cited by 14 publications

References 6 publications

Software Crowdsourcing Task Allocation Algorithm Based on Dynamic Utility

Software Crowdsourcing Task Allocation Algorithm Based on Dynamic Utility

Tracing Topic Transitions with Temporal Graph Clusters

Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

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